Harnessing Mobile Technology to Reduce Mental Health Disorders in College Populations
Phase: NA
Conditions: Anxiety Disorders, Depressive Disorder, Eating Disorders
Sponsor: Washington University School of Medicine

 
 
 
 
 
 
______________________________________________________________________________________ 
 
1
 
 
 
Harnessing Mobile Technology to Reduce Mental Health Disorders 
in College Populations 
 
Principal Investigators:   
Denise Wilfley, PhD – Washington University School of Medicine 
Craig Barr Taylor, MD – Palo Alto University 
Michelle Newman, PhD – Pennsylvania State University 
Daniel Eisenberg, PhD – University of California, Los Angeles  
 
 
 
Version: 9 
FEBRUARY 7, 2022 
 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
2
 
TABLE OF CONTENTS 
 
A LIST OF ABBREVIATIONS .................................................................................................................. 4 
B INTRODUCTION .................................................................................................................................... 4 
A1 
STUDY ABSTRACT ......................................................................................................................... 4 
A2 
PRIMARY HYPOTHESIS ................................................................................................................... 5 
B 
BACKGROUND .................................................................................................................................. 5 
B1 
PRIOR LITERATURE AND STUDIES .................................................................................................. 5 
B2 
RATIONALE FOR THIS STUDY ......................................................................................................... 6 
C 
STUDY OBJECTIVES........................................................................................................................ 6 
C1 
STUDY AIMS .................................................................................................................................. 6 
C2 
RATIONALE FOR THE SELECTION OF OUTCOME MEASURES ........................................................... 6 
D 
STUDY DESIGN ................................................................................................................................. 7 
D1 
OVERVIEW OR DESIGN SUMMARY ................................................................................................. 7 
D2 
SUBJECT SELECTION AND WITHDRAWAL....................................................................................... 9 
2.a 
Inclusion Criteria ....................................................................................................................10 
2.a 
Exclusion Criteria ....................................................................................................................10 
2.b 
Ethical Considerations ............................................................................................................10 
2.c 
Subject Recruitment Plans and Consent Process ....................................................................12 
2.d 
Randomization Method ............................................................................................................13 
2.e 
Risks and Benefits ....................................................................................................................13 
2.f 
Early Withdrawal of Subjects ......................................................................................................15 
2.g 
When and How to Withdraw Subjects ......................................................................................15 
2.h 
Data Collection and Follow-up for Withdrawn Subjects .........................................................15 
D3 
STUDY INTERVENTION ..................................................................................................................15 
3.a 
COVID-19 Specific SilverCloud Module .................................................................................16 
E 
STUDY PROCEDURES ....................................................................................................................17 
E1 
SCREENING FOR ELIGIBILITY ........................................................................................................17 
E2 
SCHEDULE OF MEASUREMENTS ....................................................................................................17 
2.a 
Data Collection Procedures for Adverse Events .....................................................................18 
2.b 
Reporting Procedures ..............................................................................................................19 
2.c 
Adverse Event Reporting Period ..............................................................................................19 
2.d 
Post-study Adverse Event ........................................................................................................19 
E3 
STUDY OUTCOME MEASUREMENTS AND ASCERTAINMENT ..........................................................19 
F 
STATISTICAL PLAN ........................................................................................................................19 
F1 
SAMPLE SIZE DETERMINATION AND POWER .....................................................................................19 
F2 
INTERIM MONITORING AND EARLY STOPPING ..................................................................................20 
F3 
ANALYSIS PLAN ................................................................................................................................20 
F4 
MISSING OUTCOME DATA .................................................................................................................22 
G 
DATA HANDLING AND RECORD KEEPING .............................................................................22 
G1 
CONFIDENTIALITY AND SECURITY ................................................................................................22 
G2 
TRAINING......................................................................................................................................23 
G3 
PERFORMANCE MONITORING .......................................................................................................23 
H 
STUDY MONITORING, AUDITING, AND INSPECTING ..........................................................23 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
3
H1 
STUDY MONITORING PLAN ...........................................................................................................23 
I 
STUDY ADMINISTRATION ...........................................................................................................24 
I1 
ORGANIZATION AND PARTICIPATING CENTERS .................................................................................24 
I2 
FUNDING SOURCE AND CONFLICTS OF INTEREST ..............................................................................25 
I3 
COMMITTEES .....................................................................................................................................25 
I4 
SUBJECT STIPENDS OR PAYMENTS ....................................................................................................25 
I5 
STUDY TIMETABLE ...........................................................................................................................26 
J 
PUBLICATION PLAN ......................................................................................................................27 
K 
REFERENCES ...................................................................................................................................27 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
4
A List of Abbreviations 
Co-I 
 
Co-Investigator 
EDs 
 
Eating Disorders 
NIH 
 
National Institute of Health 
PAU 
 
Palo Alto University 
PSU 
 
Pennsylvania State University 
PI 
 
Principal Investigator 
UM 
 
Regents of the University of Michigan 
WUSM 
 
Washington University School of Medicine 
B Introduction 
 
A1 Study Abstract 
The prevalence of mental health problems among college populations has risen steadily 
in recent decades, with one-third of today’s college students struggling with anxiety, 
depression, or an eating disorder. Yet, only 20-40% of college students with mental 
disorders receive treatment. Inadequacies in mental health care delivery result in 
prolonged illness, disease progression, poorer prognosis, and greater likelihood of 
relapse, highlighting the need for new approaches to detect mental health problems and 
engage students in services. 
 
Our interdisciplinary research team has developed a transdiagnostic, scalable, mobile 
health prevention and intervention platform. This platform uses population-level 
screening to engage college students in tailored services that address common mental 
health problems. This evidence-based approach to care delivery addresses comorbid 
mental health issues and uses personalized screening and intervention to increase 
service uptake, enhance engagement, and improve outcomes. Further, our service 
delivery model harnesses the expertise of our team of leaders in behavioral science, 
college student mental health, technology, and health economics, and bridges our 
team’s work over the past 25 years in successfully implementing a population-based 
screening program in over 160 colleges and demonstrating the effectiveness of Internet-
based programs for targeted prevention and intervention for anxiety, depression, and 
eating disorders in over 40 colleges. We propose to test the impact of this mobile mental 
health platform for service delivery in a large-scale trial across 20 colleges. Students 
who screen positive or at high-risk for clinical anxiety, depression, or eating disorders 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
5
(excluding anorexia nervosa, for which more intensive medical monitoring is warranted), 
which account for a substantial proportion of the mental health burden on college 
campuses, and who are not currently engaged in mental health services (N=7,884; of 
146,000 initially screened) will be randomly assigned to: 1) intervention via the mobile 
mental health platform; or 2) referral to usual care (i.e., campus health or counseling 
center). Our comprehensive mental health care platform can yield clinical benefit to 
students, appeal to university stakeholders, minimize barriers to implementation 
sustainability on campuses, and produce an economic return on investment compared to 
usual care.  This population-level approach to service engagement has the potential to 
improve mental health outcomes for the 20+ million students enrolled in U.S. colleges 
and universities. 
 
A2 Primary Hypothesis 
The mobile mental health platform will yield substantially higher and earlier uptake of 
services compared to referral to usual care. 
 
B Background 
 
B1 Prior Literature and Studies 
Prevalence of mental health problems among college students has risen steadily in 
recent decades.9 In national epidemiologic studies, past-year prevalence of mood and 
anxiety disorders was 11% and 12%, respectively,16 and 9% of college students 
screened positively for an eating disorder (ED).21 Among students with probable 
disorders, only 20-40% received treatment,16,22 and this number was even lower among 
students of color and those from low-income families.17 With over 20 million people 
enrolled in U.S. postsecondary education,23 we can infer an extremely large treatment 
gap24,25—there are at least 6.5 million college students with mental disorders, and at 
least 4 million college students with untreated mental disorders. Early intervention is 
particularly important during college ages of 18-24, when mental illnesses account for 
the largest burden of any disease.26 Untreated symptoms become more frequent, 
severe, and persistent over time.27-29 Students often have difficulty getting appropriate 
health care,30 which can have lasting consequences on functioning, physical health, 
suicidality, social relationships, and educational attainment.14,15,31 Although many 
campus counseling and health centers have increased their capacity to provide 
services,7 there remain major limitations to access and delivery of mental health services 
in college populations. For students, important barriers include lack of problem 
recognition, lack of time, lack of urgency to seek services, and stigma.32 Increasing the 
number of providers, by itself, neither addresses the large number who wait until they 
reach a crisis level to seek mental health support, nor alleviates stigma or campus  
leaders’ concerns about continuing to pour more resources into services without 
appreciable slowing in demand.7 These factors highlight the need for a more efficient, 
proactive, and accessible service delivery model for managing mental health in college 
student populations through combined targeted prevention and  intervention. 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
6
B2 Rationale for this Study 
mHealth technologies have potential to improve mental health care on college 
campuses by overcoming treatment barriers and increasing efficiency. Such 
interventions can offset in-person clinical demands, increase access, enhance treatment 
precision, and reduce costs.35 Mobile technologies are efficacious for screening and 
treatment of anxiety, depression, and EDs across settings and populations, including in 
college students1,2,6,36-38 for whom smartphone use is ubiquitous,39 with high acceptability 
given their convenience and anonymity. However, these interventions have been 
primarily delivered independently. A key challenge is delivering these technologies 
effectively and in combination at a population level. Such an approach would link 
screening with intervention, maximize engagement, and address comorbid problems. 
 
C Study Objectives 
 
C1 Study Aims 
Our study aims are as follows: 
1) Aim 1: Examine uptake (i.e., individuals beginning treatment) of the mobile 
mental health platform compared to referral to usual care. 
2) Aim 2: Evaluate the effectiveness of the mobile mental health platform, compared 
to usual care, in (2a) reducing the number of individuals with mental health 
disorders (primary outcome); (2b) reducing disorder-specific symptoms 
(secondary outcome); and (2c) improving quality of life and functioning 
(secondary outcome).  
3) Aim 3: (3a) Examine if the mobile intervention changes targets previously found 
to be associated with outcomes, both transdiagnostic (i.e., decreased 
dysfunctional cognitions, increased use of CBT skills) and disorder-specific (i.e., 
reduced avoidance for anxiety, increased behavioral activation for depression, 
reduced dietary restraint and weight/shape concerns for EDs); (3b) determine if 
changes in targets are associated with clinical benefit; and (3c; exploratory) 
identify other putative mediators of change (e.g., early engagement in help 
services, rapid response), (3d; exploratory) within-mobile program predictors of 
outcome (e.g., sessions completed), and (3e; exploratory) treatment moderators.  
4) Aim 4 Evaluate stakeholder-relevant outcomes: (4a)  costeffectiveness; (4b) 
students’ academic performance; and (4c) attitudes.  
 
C2 Rationale for the Selection of Outcome Measures 
 
As detailed above, we’ll be examining uptake of the mobile mental health platform 
compared to referral to ususal care, as well as effectiveness of the platform in reducing 
the number of individuals with mental health disorders; reducing disorder-specific 
symptoms; and  improving quality of life and functioning. These are critical outcomes as 
they will tell us whether the mobile mental health platform is not only more likely to be 
used by students (uptake) but also whether it is effective on several dimensions.  
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
7
We also propose several mediators, which fall into: 1) primary intervention 
targets/mechanisms, considered most essential to outcomes (some of which are 
transdiagnostic and some disorder-specific; and 2) other mediators—early engagement 
in services and rapid response. CBT theory posits that the most 
important ways to change symptoms are by changing cognitions and/or changing 
behaviors.  Thus, our transdiagnostic targets are decreased dysfunctional cognitions and 
increased use of CBT skills. Empirical support for cognitive changes suggests that they 
are central to reduction in anxious113 and depressive114 symptoms. For EDs, although 
only a few studies have examined change in cognition,115 it is worth including given its 
emphasis in CBT theory.116 There is evidence that acquisition of CBT skills (e.g., 
challenging thoughts) is another critical target at least for anxiety and depression.117-122 
Our disorder-specific, evidence-based targets are as follows: reducing avoidance, 
associated with changing anxiety;123-125 increasing behavioral activation, associated with 
reducing  depressive symptoms126-128; and reducing dietary restraint and weight/shape 
concerns for reducing EDs.61,102,115,129 We will examine whether the mHealth platform 
leads to change in the targets listed above and whether target change is associated with 
clinical benefit. Use of any potentially helpful services (e.g., Lantern, self-help, face-to-
face therapy, medication) is also likely to lead to change, particularly in the first 6 weeks. 
Thus, while not a specific target of intervention, early engagement in help services is a 
potential mediator of symptom reduction130 and will be tested. Rapid response in 
symptoms, with rapid response usually defined as ≥50% reduction in symptoms 
occurring within the first 4-6 weeks of treatment,131,132 predicts longer-term outcome in 
anxiety, depression, and EDs and is considered a mediator.97,100-102,133-138 Within-mobile 
program indices will be tested as predictors of outcome, including sessions completed, 
number of messages sent by user and coach, techniques completed, time spent with the 
program, and coach personalization (i.e., assessed through users’ perception and 
coding a subset of messages. Finally, data will be used to explore moderators— 
subgroups for whom further tailoring or more intensive services are needed in future 
iterations. Moderators exploratory and based on prior work; they are: gender, 
race/ethnicity, expectancy/credibility,139-143 symptom severity,142,144-147 duration of 
problem,142,145,148-150 comorbidity,142,145,149,151-153, disorder for which the person sought 
treatment,144,152,154 degree of cognitive distortion,142 impairment,145 family income,142 and 
motivation.155 
 
D Study Design  
 
D1 Overview or Design Summary 
At each of the participating campuses, we will recruit students from the undergraduate 
population. Some campuses may decide to provide us with with a random sample 
(requesting from the Registrar [or equivalent campus unit] which  isoversampled by 
gender, race/ethnicity, and socioeconomic status [e.g., Pell grant recipients] to ensure 
that participant samples are diverse and representative on these dimensions). 
 
Students will be recruited via emails to their campus email address to participate in the 
screen.  
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
8
Students will be asked at the start of the screen if they would like to opt out of receiving 
information about potential future studies. We will create an entire new registry of 
students from those who do not select to opt out of future studies, ensuring we will not 
be able to contact those who opted out of future contact from the new registry.  
 
Following the screen students will receive personalized feedback about their results. The 
personalized feedback will be automated through Qualtrics,allowing for an efficient yet 
individualized feedback approach. The personalized feedback will include either an end 
of survey message thanking the individual for their participation (and a referral for 
treatment if applicable) or an invitation to participate in the full study as follows: 
1. No symptoms or low risk for depression, anxiety, and EDs - they will not be 
eligible to participate in the next phase of the study. 
2. Low risk with trauma, insomnia, or substance abuse - receive referral for clinical 
evaluation and intervention. 
3. At high risk for or with clinical anxiety, depression, or an ED not currently in 
treatment; own a smartphone - will be invited to participate in phase 2 of the 
study. 
4. At high risk for or with clinical anxiety, depr ession, or an ED currently in 
treatment and/or without a smartphone – will be encouraged to continue in 
treatment provided with a referral for clinical evaluation and intervention. 
5. Anorexia nervosa - receive referral for clinical evaluation and intervention. 
 
Following the personalized feedback, students meeting study eligibility criteria will 
receive a brief statement of eligibility for and description of the study purpose and 
duration, activities involved, and assessment time points. Students will be asked to 
confirm their willingness to continue. If they agree students will be asked for their email 
address and phone numbers, and will be provided a link to the Phase 2 survey. After 
giving online consent, participants will be randomized via a code through Qualtrics into 
one of two conditions:  
1) Intervention: receiving access to our mobile coached self-help program for 6 
months.  
2) Control: Receiving information on how to receive treatment at the student’s 
counseling center on campus.  
 
Participants will then complete the baseline survey, which will assess treatment 
expectancy/credibility, cognitive distortion, impairment/quality of life, motivation for 
treatment, and mental health treatment utilization. After completing these measures 
students will be made aware of the condition they were randomized into and asked to 
complete a short Treatment Credibility and Expectancy Questionnaire. 
 
Individuals who choose not to continue will receive their screen feedback through 
Qualtrics, a recommendation to seek care, and a campus-specific list of resources.  
 
After completing the baseline survey participants will receive an email and text message 
with information about their screen responses and either a link to the mobile intervention 
or information on how to access clinical services, based on their randomized condition. 
Individuals who screen positive or at high risk for more than one of the three mental 
health disorders (anxiety, depression, EDs) will be asked to select which problem is 
most bothersome or impairing to their everyday functioning, which has been shown to 
correlate with provider ratings,and will then be directed to focus primarily on that problem 
using either the mobile app or in-person services. 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
9
 
All students in the usual care condition will receive a recommendation to seek care and 
will be given a list of resources available through their respective health or counseling 
center, including contact information to make an appointment and any other relevant 
resources on campus. Participants who will be completing the mobile interventions will 
be given a link to the program, hosted on a platform by our technology partners 
SilverCloud Health, and a program description. They will also be provided with 
instructions to download the SilverCloud Health program app, through which they can 
access the intervention, as well. Students are encouraged to log in to the intervention, 
create an anonymous username and password, and begin the program.  
 
Both intervention and control groups will be asked to complete assessments at 6 weeks, 
6 months, and 2 years after completing the baseline screen. 6 week assessments will 
ask participants to complete assessments of anxiety, depression, EDs, motivation for 
treatment, and mental health treatment utilization. The 6 month and 2-year follow-ups 
will include assessments of anxiety, depression, EDs, comorbidities, impairment/quality 
of life, motivation for treatment, mental health treatment utilization, and screening and 
treatment feedback. In addition, alternate contact information will be requested at 
baseline, 6, and 12 month follow-ups in order to maximize the likelihood that participants 
can be reached at the next follow-up assessment, including cell phone number for 
receiving SMS text-message reminders for follow-up surveys to be sent from the 
Qualtrics platform. Follow-up emails and SMS may also use images or other media to 
increase the students’ attention and likelihood of reponse. In order to increase the 
response rate at 2-year follow-up the study team may call participants to remind them to 
complete the survey, the study team may leave a voicemail if the participant does not 
respond. All assessments will be conducted using Qualtrics survey platform. 
 
Participants will be compensated by receiving an electronic gift card for completing the 
Baseline ($5), 6 week ($5), 6 month ($10), and 2-year ($20) follow-up assessments. 
Particpants will be entered into a sweepstakes to win one of five $50 gift cards after 
completing the Baseline, 6 week, 6 month, and 2-year surveys. In addition, once 
particpants complete all four surveys, they will be entered into a sweepstakes to win one 
of ten $100 gift cards. 
All students who receive a recruitment email will be automatically entered into a 
sweepstakes for one of 40 $100 electronic gift card prizes. 
 
In order to assess mental health treatment utilization, in addition to assessing this via- 
self-report, we will obtain aggregate counts of participants in therapy from campus 
counseling/health centers, by random assignment group at baseline, 6 months, and 2 
years. To assess academic outcomes, including term by-term GPA and enrollment 
status (to assess drop-out), at the end of the 2 years we will obtain aggregate data of 
participants from Registrars by semester/term to include GPA and attrition rate by 
condition. 
 
D2 Subject Selection and Withdrawal  
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
10
2.a 
Inclusion Criteria  
Screening survey inclusion criteria: Undergraduate students at participating colleges and 
universities who are 18 years old and older. 
 
Full study inclusion criteria:  
 
Students who screen has high risk or clinical/subclinical for anxiety, depression, 
and, EDs 
 
Students who are not currently in treatment, i.e., in the past month. 
 
Own a smartphone. 
 
Students who endorse suicidal ideation in the baseline screening assessment will be 
given a referral to seek in-person care, however, we decided not to exclude students 
with suicidality, given that only 20-40% of college students with mental health problems 
receive treatment. As such, we deemed it insufficient to exclude such individuals from 
receiving the intervention and only providing an in-person referral, given the goal of this 
proposal to narrow the treatment gap for mental health problems among college 
students. 
2.a 
Exclusion Criteria  
For the full study we will exclude students who do not own a smartphone since the 
intervention is a mobile app. We will also excludes students who are currently engaged 
in mental health treatment given the goal to increase access to services for those not 
receiving help. 
2.b 
Ethical Considerations  
All key personnel involved in the design or conduct of research involving human subjects 
will receive the required education on the protection of human research participants prior 
to the start of the study. Participants will be informed that they do not have to answer 
any questions that make them uncomfortable. There are minimal risks for participants in 
the mobile health intervention. Coaches in SilverCloud Health will be recruited at the 
study four participating sites. Coaches will be students in a graduate program in 
psychology, counseling, or social work. Coaches will be trained and under the close 
supervision of a coach supervisor, at WUSM and PAU who licensed psychologists, as 
well as the Fidelity Monitoring Center.  
 
Given national data showing that nearly 10% of students have seriously considered 
suicide in the past year and that rates of suicide on university campuses continue to 
increase, suicidality will be consistently monitored throughout the study, including within 
the mobile intervention itself and through the completion of the baseline and 6-week, 6-
month, and 2-year follow-up assessments. Students who endorse any suicidality on 
these assessments will receive immediate follow-up, including a referral to seek in-
person care. Despite the perceived risk that assessing suicidality will increase suicidality, 
research supports that asking about this does not increase suicidality and, among those 
who may endorse active suicidal plans or intent, may lead to improvements in health 
seeking behaviors and increased safety. Notably, we decided not to exclude students 
with suicidality, given that only 20-40% of college students with mental health problems 
receive treatment. As such, we deemed it insufficient to exclude such individuals from 
receiving the intervention and only providing an in-person referral, given the goal of this 
proposal to narrow the treatment gap.  

 
 
 
 
 
 
______________________________________________________________________________________ 
 
11
 
Participant safety is of utmost clinical import, and several safety precautions and 
procedures will be implemented. 
1) SilverCloud Health flags and notify coaches when a user responds positively to the 
PHQ-9 item about suicidality. Coaches will monitor the platform weekly to assess for 
significant changes in symptoms (e.g., increased purging). Any participant deemed 
unsafe or needing more intensive clinical intervention will be given a referral by their 
coach as well as contacted directly via telephone. Appropriate follow-up (e.g., contacting 
the university's on-call clinical staff or local police) will be implemented if participants are 
deemed to be at imminent risk and no response is provided by the participant indicating 
their safety. This protocol is to safeguard against concerns with keeping individuals in 
guided self-help care when more intensive services are warranted. 
2) Individuals who remain in the clinical symptom range, in both the intervention and 
control conditions, at the 6-month or 2-year follow-up assessments, will be offered a 
referral for in-person treatment. For individuals in the intervention condition, in line with a 
stepped care model, those who do not demonstrate symptom improvement by 6 weeks 
will be referred to in- person care but can still engage in the program. 
 
In the case of imminent risk, such as in the extremely rare case of a user reporting plans 
of suicide or homicide, there are several steps that coaches for students engaging in the 
mobile program are mandated to take as mandated reporters. First, coaches contact a 
coach supervisor and the Fidelity Monitoring Center either by phone or an email marked 
URGENT to consult about how to proceed as soon as possible. If coaches do not 
receive a response within an hour, they will reach out to a back-up coach supervisor. 
Second, coaches identify the appropriate local agency such as the campus police at the 
user’s college or university to prepare for possibly making a report. Next, they provide 
the user with a variety of emergency options such as counseling center information, 
suicide hotline phone number, emergency room location, or local police information and 
encourage clients to seek additional support or hospitalization through these options. 
Coaches also gather relevant information by reviewing the coaching record for all 
identifying information available such as name, age, phone number, and email address 
to provide to the agency when making a report. If it is deemed appropriate by the 
coaching supervisor, coaches call in the report as soon as possible to the identified local 
agency. They also follow up with the user, express concern, provide support, and let 
him/her know a report has been made. In addition, coaches follow up with the coach 
supervisor and the Fidelity Monitoring Center via email to let him/her know the report 
was made. Finally, coaches continue to monitor the user for emerging or worsening 
risks, as demonstrated by information the user inputs into the program or messages to 
the coach, and they continue to keep in close contact with the coaching supervisor. 
Should the user continue to worsen and be deemed at continued imminent risk, the 
coach and coach supervisor may make a decision to withdraw the participant from the 
program. 
 
In the case that individuals endorse criteria for possible AN (low weight and significant 
weight shape concerns) at the 6-month or 2-year follow-up assessments, they will be 
offered a referral for in-person treatment. 
 
The Fidelity Monitoring Center will conduct fidelity checks on the coaching data from 
SilverCloud Health to ensure that coaches are following protocol (e.g., referrals given if 
lack of symptom improvement). In general, the Fidelity Monitoring Center will oversee 
regular review of coaches using quantitative and qualitative metrics that include tone and 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
12
style (authentic, personalized) of their interactions with clients, content (depth, goal 
orientation), efficiency (response time), user engagement, outcomes, and accuracy rate 
for risk management. Coaches that do not meet the metrics will be put on a performance 
improvement plan and may be terminated if improvement is not demonstrated. 
 
IRB of Record  
The use of a single IRB model is designed to enhance and streamline the process of 
IRB review and reduce inefficiencies without compromising ethical principles and 
protections within this multi-site clinical study. Each site signs an IRB Authorization 
Agreement to document the delegation of responsibilities of IRB review to WUSM and 
demonstrate WUSM’s acceptance of these responsibilities. 
 
2.c 
Subject Recruitment Plans and Consent Process 
Each participating school will provide us with a database of students from their registrar 
or academic information office. The database will contain a full population sample or 
random sample of undergraduate students. The database will include demographic 
information about students such as first name, email address, sex, race/ethnicity. 
 
Students will be recruited via emails to their campus email address to participate in the 
screenning assessment. Students will first receive a brief “pre-notification” email, which 
lets them know that an invitation to the study will be coming soon. Two to three days 
later, we will send the screening assessment recruitment email with a link to the online 
survey, and we will follow-up with reminder emails to non-responders. The emails will 
note that students can reply to decline participation (and any further reminders); they 
may also select this option on the consent form page of the screening survey. 
 
Once students click on the survey link, students will be asked to review the consent 
information sheet, they will have all the time they need to review it. Full disclosure of the 
purpose of the study, the benefits and risks to individuals who participate, and the 
confidential nature of information obtained during the study will be explained to 
participants. Participants will be aware from the outset that they could be eligible for 
phase 2 of the study. Participants will be consented according to the policies and 
procedures of the WUSM IRB, the IRB of Record for the study. A trained study staff 
member will be available to students via phone to answer any questions they may have 
about their participation in the study.  
 
We will follow-up with reminder emails to non-responders. The emails will note that 
students can reply to decline participation (and any further reminders); they may also 
select this option on the consent form page of the baseline survey. 
 
For phase 2 eligible participants,  following the personalized feedback , they will receive 
a brief statement of eligibility for and description of the study purpose and duration, 
activities involved, and assessment time points. Students will be asked to confirm their 
willingness to continue. If they agree students will be asked for their email address and 
phone numbers, and will be provided a link to the Phase 2 baseline survey. People who 
do not complete the baseline survey will be sent an email reminder to complete the 
survey. 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
13
Once students click on the survey link, students will be asked to review the informed 
consent before continuing with the baseline survey. Student will have all they time they 
need to review it. Full disclosure of the purpose of the study, the benefits and risks to 
individuals who participate, and the confidential nature of information obtained during the 
study will be explained to participants. Participants will be aware from the outset that 
they could be randomly assigned to a mobile health platform or referral to usual care. 
They will also be aware that their expected participation will last up to two years, 
including three additional follow-up assessments. Participants will be consented 
according to the policies and procedures of the WUSM IRB, the IRB of Record for the 
study. A trained study staff member will be available to students via phone to answer 
any questions they may have about their participation in the study.  
 
2.d 
Randomization Method 
In phase 2, participants will be randomized after giving online consent, participants will 
be randomized via a code through Qualtrics into one of two conditions:  
1) Intervention: receiving access to our mobile coached self-help program for 6 
months.  
2) Control: Receiving information on how to receive treatment at the student’s 
counseling center on campus. 
Participants will be made aware of the condition they were randomized into after 
completing the baseline survey. 
2.e 
Risks and Benefits 
Risks 
 
There are minimal risks for participants. 
 
The assessment process may carry potential risks. For example, some of the questions 
may be upsetting to participants, and there may be some risk of breach of confidentiality 
if the suicide or crisis assessment protocols need to be deployed. 
 
There are no high-risk or hazardous aspects of our proposed mobile intervention 
program; however, we recognize that programs to treat mental health problems may 
promote increased focus on mental health symptoms, more than that which may exist 
before starting the program. 
 
Coaches in SilverCloud Health will be recruited from the institutions of the study team 
members (i.e., WUSM, PAU, PSU, UM). Coaches will be students in a graduate program 
in psychology, counseling, or social work. Coaches will be trained and under the close 
supervision of coach supervisors, at WUSM and PAU, as well as the Fidelity Monitoring 
Center. Given national data showing that ~10% of students have seriously considered 
suicide in the past year, suicidality will be consistently monitored throughout the study, 
including within the program itself and through the completion of all assessments. 
Students who endorse any suicidality on these assessments will receive immediate 
follow-up, including a referral to seek in- person care. Participant safety is of utmost 
clinical import, and several safety procedures will be implemented. 
 
1) SilverCloud Health flags and notify coaches when a user responds positively to the 
PHQ-9 item about suicidality. Coaches will monitor the platform weekly to assess for 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
14
significant changes in symptoms (e.g., increased purging). Any participant deemed 
unsafe or needing more intensive clinical intervention will be given a referral by their 
coach as well as contacted directly via telephone. Appropriate follow-up (e.g., contacting 
the university's on-call clinical staff or local police) will be implemented if participants are 
deemed to be at imminent risk and  no response is provided by the participant indicating 
their safety. This protocol is to safeguard against concerns with keeping individuals in 
guided self-help care when more intensive services are warranted. 
2) Individuals who remain in the clinical symptom range, in both the intervention and 
control conditions, at the 6-month or 2-year follow-up assessments, will be offered a 
referral for in-person treatment. For individuals in the intervention condition, in line with a 
stepped care model, those who do not demonstrate symptom improvement by 6 weeks 
will be referred to in- person care but can still engage in the program. 
 
In the case of imminent risk, such as in the extremely rare case of a user reporting plans 
of suicide or homicide, there are several steps that coaches are mandated to take as 
mandated reporters. First, coaches contact a coach supervisor and the Fidelity 
Monitoring Center either by phone or an email marked URGENT to consult about how to 
proceed as soon as possible. If coaches do not receive a response within an hour, they 
will reach out to a back-up coach supervisor. Second, coaches identify the appropriate 
local agency such as the campus police at the user’s college or university to prepare for 
possibly making a report. Next, they provide the user with a variety of emergency 
options such as counseling center information, suicide hotline phone number, 
emergency room location, or local police information and encourage clients to seek 
additional support or hospitalization through these options. Coaches also gather relevant 
information by reviewing the coaching record for all identifying information available such 
as name, age, phone number, and email address to provide to the agency when making 
a report. If it is deemed appropriate by the coaching supervisor, coaches call in the 
report as soon as possible to the identified local agency. They also follow up with the 
user, express concern, provide support, and let him/her know a report has been made. 
In addition, coaches follow up with the coach supervisor and the Fidelity Monitoring 
Center via email to let him/her know the report was made. Finally, coaches continue to 
monitor the user for emerging or worsening risks, as demonstrated by information the 
user inputs into the program or messages to the coach, and they continue to keep in 
close contact with the coaching supervisor. Should the user continue to worsen and be 
deemed at continued imminent risk, the coach and coach supervisor may make a 
decision to withdraw the participant from the program. 
 
 
In addition, Dr. Newman will hold monthly meetings with coach supervisors. 
Furthermore, the Fidelity Monitoring Center will conduct fidelity checks on the coaching 
data from SilverCloud Health to ensure that coaches are following protocol (e.g., 
referrals given if lack of symptom improvement). In general, the Fidelity Monitoring 
Center will oversee regular review of coaches using quantitative and qualitative metrics 
that include tone and style (authentic, personalized) of their interactions with clients, 
content (depth, goal orientation), efficiency (response time), user engagement, 
outcomes, and accuracy rate for risk management. Coaches that do not meet the 
metrics will be put on a performance improvement plan and may be terminated if 
improvement is not demonstrated.  
 
Benefits 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
15
The benefits to participants in this study and to society are expected to be great. In 
terms of potential benefits to participants, these include: improved uptake of services, 
reduced anxiety, depressive, and/or ED symptoms, and improved quality of life. The 
treatment may also improve academic impairment and reduce likelihood of dropout from 
college as well as stress on the individual posed by the burden of mental health 
problems. In addition, it is possible that skills to solve ongoing problems that may 
maintain mental health problems may improve. The growing prevalence and burden of 
mental health problems on the college campus highlight the need for a new approach for 
detecting mental health problems and engaging college students in services. Therefore, 
the potential risks that are associated with this study are reasonable when considering 
the many benefits that the participants and society may gain. 
2.f 
Early Withdrawal of Subjects  
The recruitment emails will note that students can reply to decline participation (and any 
further reminders); they may also select this option on the consent form page of the 
screening survey. 
 
2.g 
When and How to Withdraw Subjects  
Taking part in this research study is voluntary. Participants may choose not to take part 
in this research study or may withdraw their consent at any time. They may withdraw by 
telling the study team they are no longer interested in participating in the study or they 
may send in a withdrawal letter. There will be no penalty or loss of benefits to which they 
were otherwise entitled. 
 
2.h 
Data Collection and Follow-up for Withdrawn Subjects  
 
When a participant withdraws from the study, the research team will stop collecting data 
from them. 
D3 Study Intervention 
 
SilverCloud Health Guided Self-Help Mental Health Programs 
 
The SilverCloud Health platform provides three self-help guided interventions, one for 
each disorder (both for those with clinical symptoms and at high risk). The interventions 
were derived from evidence based guided self-help treatments that were efficacious in 
traditional efficacy trials, specifically for anxiety, depression, and EDs. Each session 
takes about 20 minutes. To maximize efficacy, key targets (i.e., decreased avoidance for 
anxiety disorders; increased behavioral activation for depression; decreased dietary 
restraint and weight/shape concerns for EDs; and decreased dysfunctional thoughts and 
increased use of Cognitive behavioral treatment skills for all three disorders) are 
addressed, which include the following content to address the targets: Anxiety: 
Psychoeducation on anxiety and avoidance, goal setting, self-monitoring and cognitive 
restructuring (to identify and alter negative automatic thoughts), exposure therapy, 
mindfulness/relaxation. Depression: Psychoeducation on the cognitive-behavioral theory 
of depression, goal setting, identifying and engaging in enjoyable activities (behavioral 
activation), emotion regulation and distress tolerance skills, self-monitoring and cognitive 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
16
restructuring (to identify and alter negative automatic thoughts). EDs: Psychoeducation 
on the cognitivebehavioral theory of EDs, self-monitoring, goal setting, meal planning 
and tracking, cognitive and behavioral strategies to improve body image, eating, and 
coping skills.  
 
Participants completing the mobile intervention who screened positive or at high risk for 
2 or more disorders (i.e., of depression, anxiety, and EDs) will be asked to choose a 
focus based on which problem is most bothersome or impairing to their everyday 
functioning, and will be assigned that program as their primary track. Incorporating 
participant preference in our approach is designed to enhance the students' involvement 
as stakeholders and to increase their engagement. As they progress through the 
intervention, they also will be assigned the components presumed to be essential to 
intervention effects for the comorbid disorder. 
 
Participants completing the mobile intervention will receive the support from a coach. 
Their role includes supporting and enhancing user motivation; monitoring progress; 
facilitating goal setting and offering accountability; providing feedback on technique 
usage and encouraging practice; answering user questions; and monitoring 
for/responding to clinical risk. Communication with users is primarily via two-way, 
asynchronous messaging, with optional, supplementary phone calls to enhance goal 
setting. Coach messaging is done via a web-based "dashboard," and delivered to users 
within the SilverCloud Health  app. Coaches have participants' baseline screen results 
so they can personalize treatment and help the user apply skills learned to comorbid 
problems. Moreover, coaches will monitor symptoms using the following assessments 
built into the SilverCloud Health programs: 
1) For individuals with depression, depression is assessed weekly using an 
abbreviated version of the measure used for assessing depression at baseline 
(i.e., PHQ-2). 
2) For individuals with anxiety, anxiety is assessed weekly using a short, validated 
measure of anxiety (i.e., GAD-2).  
3) For individuals with eating disorders, weekly monitoring includes three questions 
based on the EDE-Q assessing eating disorder behaviors (i.e., binge eating, 
purging) and weight/shape concerns. 
 
Note: When individuals have more than one of these problems, they complete weekly 
monitoring for each comorbidity. 
 
If the coach feels the online program cannot adequately address comorbidity, 
participants are referred to appropriate campus/community resources. 
 
The program will be accessible via smart phone app or their desktop. Program access 
will be password protected. 
 
3.a 
COVID-19 Specific SilverCloud Module 
 
During the COVID-19 pandemic, coaches in the SilverCloud intervention will be 
randomized into either condition: 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
17
1. Give the COVID-19 modules to their users in addition to their assigned 
SilverCloud modules to treat depression, anxiety, and/or eating disorders, 
following the same schedule as for transdiagnostic modules. 
2. Only providing their users with their assigned SilverCloud modules to treat 
depression, anxiety, and/or eating disorders.  
 
E Participants will not be aware of this randomization to 
prevent bias when answering surveys.Study 
Procedures  
 
E1 Screening for Eligibility 
Eligibitlity will be assessed using the measures and cut-offs in the table below. 
 
 
 
Participants will also be asked if they own a smartphone and if they are currently in 
treatment for mental health. 
 
E2 Schedule of Measurements 
Participants will be assessed at screening, baseline, 6 weeks, 6 months, and 2 years. All 
surveys will be administered via Qualtrics. Participants will be compensated by receiving 
an electronic gift card for completing the Baseline ($5), 6 week ($5), 6 month ($10), and 
2-year ($20) follow-up assessments. Particpants will be entered into a sweepstakes to 
win one of five $50 gift cards after completing the Baseline, 6 week, 6 month, and 2-year 
surveys. In addition, once particpants complete all four surveys, they will be entered into 
a sweepstakes to win one of ten $100 gift cards. 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
18
All students who receive a recruitment email will be automatically entered into a 
sweepstakes for one of 40 $100 electronic gift card prizes. 
 
See table below for schedule of assessments: 
 
 
2.a 
Data Collection Procedures for Adverse Events 
For the purpose of this study, adverse events will be defined as unanticipated problems 
involving risks to the study participant. A serious adverse event will be defined as any 
untoward occurrence that results in death, is life threatening, or creates persistent and 
 
Baseline 
Screen 
Intervention 
Period 
Post-Screen 
Assessments 
Primary Purpose 
6 wks 
6 mos 
2 yrs 
  
PARTICIPANTS
Demographics
x
Mod
Expectancy/credibility 
x 
 
 
 
 
Mod 
Degree of cognitive distortion 
x 
 
 
 
 
Mod 
Anxiety: Generalized anxiety; 
social phobia; panic 
x 
  
x 
x 
x 
TO/Mod (symptom severity; 
comorbidity) 
Depression 
x 
  
x 
x 
x 
TO/Mod (symptom severity; 
comorbidity)
Eating disorders 
x 
  
x 
x 
x 
TO/Mod (symptom severity; 
comorbidity)
Chronicity
x
Mod
Comorbidities: Trauma; insomnia; 
substance use
x 
  
  
x 
x 
Mod 
Impairment/quality of life
x
x
x
TO/Mod
Motivation for treatment
x
x
x
x
Mod
Mental health treatment utilization 
x 
 
x 
x 
x 
TO 
Academic impairment (obtained 
from colleges)
x 
  
  
x 
x 
TO 
Target engagement 
Transdiagnostic: dysfunctional 
cognitions; CBT skills 
Disorder-specific: avoidance; 
behavioral activation; restraint; 
weight/shape concerns
x 
  
x 
  
  
Med 
Early engagement 
 
 
x 
 
 
Med 
Rapid response 
 
 
x 
 
 
Med 
Screening and treatment 
feedback 
 
 
 
x 
x 
-- 
TREATMENT PROCESS (INTERVENTION CONDITION) 
Mobile treatment process (e.g., 
sessions completed, messages 
with coach)
  
x 
 
  
  
Pred 
Fidelity/personalization of 
coaching (i.e., user perception 
and coder ratings)
  
x 
x 
x  
  
Pred 
STUDENT MENTAL HEALTH SERVICES CENTERS 
Attitudes toward mobile mental 
health platform
x 
  
  
  
  
-- 
Perceptions of mobile mental 
health platform
  
  
  
x 
  
-- 
Intended adoption
x
--
Mental health service utilization 
x 
 
 
 
x 
TO 
Notes: Light gray shading = occurring during intervention period. Mod = moderator; TO = treatment outcome; Med = mediator; 
Pred = predictor.

 
 
 
 
 
 
______________________________________________________________________________________ 
 
19
significant disability. The initial step in reporting adverse events of any kind is to consult 
with Dr. Newman, PI at PSU, who will be providing oversight for adverse event reporting 
for the study. Additionally, adverse events will be reported to PIs at the other sites, Drs. 
Wilfley (WUSM), Eisenberg (UM) and Taylor (PAU), who will decide if the event is 
serious enough to warrant treatment discontinuation or participant withdrawal. Any 
potentially adverse events will be evaluated by the PIs within 72 hours. All serious 
adverse events will be immediately reported to the IRB of Record. All adverse events 
and study withdrawals, together with a detailed explanation of the event and withdrawal, 
will be forwarded to the Human Subjects Committee and the DSMB. In addition, at its 
regular meeting, the DSMB will summarize all adverse events of any severity, to be 
forwarded to the IRB of Record via Dr. Wilfley.  
 
2.b 
Reporting Procedures 
Because the online interventions follow standard practice guidelines that have been 
used in research trials without significant adverse events, we do not anticipate any 
significant adverse events. However, coaches will be trained to monitor for adverse 
events, including Serious Adverse Events (SAEs) such as deaths, hospitalizations, and 
life threatening events and Unanticipated Problems (UPs), and will report any potential 
adverse events immediately upon their identification to the FMC. We will also monitor for 
adverse events at all follow-up assessments and via all other communication with study 
participants. Any potentially adverse events will be evaluated by the PIs within 72 hours. 
Serious adverse events will be promptly reported to the study IRB. Study-related serious 
adverse events will be reported to the NIMH within 2 weeks; all others will be included in 
the annual report to the NIMH. 
2.c 
Adverse Event Reporting Period 
 
2.d 
Post-study Adverse Event 
 
E3 Study Outcome Measurements and Ascertainment 
Study outcomes includes the following: 
 
Reducing the number of individuals with mental health disorders. 
 
Reducing disorder-specific symptoms 
 
improving quality of life and functioning 
F Statistical Plan  
 
F1  Sample Size Determination and Power 
Sample size was based on achieving greater than 80% power to detect minimally 
meaningful differences in primary and secondary outcomes. We will include 20 colleges 
with approximately equal numbers of participants per campus, and approximately 3,154 
high risk and 3,154 clinical participants with complete follow-up data. All power estimates 
were derived from Monte Carlo simulations (a gold standard in power estimation)214,215. 
For all analyses, power estimation is based on an effect size of .2 (i.e., for the primary 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
20
outcome, a differential change of .2 in the treatment vs. control group; for moderator 
analyses, effect size was .2 for each moderation effect; for mediator analyses, each 
pathway within the mediation was estimated at .2). We thus have power to detect 
smaller differences than we anticipate based on assumptions; this extra power is 
important, given that smaller differences would still be clinically meaningful at a 
population scale, particularly considering the modes t cost of this online intervention. 
Power for all secondary outcomes was also estimated within the Monte Carlo simulation 
using all moderators within the same model; and all mediators within the same model 
targeting a .2 effect size, reflecting a conservative approach to power calculation and 
model estimation. Results of the Monte Carlo simulations suggested we had greater 
than 90% power to detect all primary outcomes and greater than 80% power to detect all 
secondary outcomes. 
F2  Interim Monitoring and Early Stopping 
The DSMB will provide oversight and ongoing monitoring of participant safety, quality of 
data collection, and integrity of the study on a yearly basis. The DSMB will receive a 
report from the study Screening Implementation and Data Coordinating Center 
approximately four weeks before each review date. These reports will include the major 
variables necessary for monitoring safety, quality of data collection, and integrity of the 
study and will include otherwise blinded outcome data. The DSMB will prepare a report 
after every meeting based on the material received from the Screening Implementation 
and Data Coordinating Center, which will be forwarded to the PIs to review at Study 
Executive Committee meetings and also forwarded to the IRB of Record by Dr. Wilfley. 
 
F3  Analysis Plan 
To evaluate efficacy of the mHealth platform vs. usual care, in engaging participants 
(Aim 1) we will use hierarchical logistic regression to examine uptake (i.e., individuals 
beginning any sort of treatment). Outcome will be at least one session of mental health 
services use (either online or in-person) assessed at 6 weeks, 6 months, and 2 years. 
Using hierarchical logistic regression, we will also examine efficacy in reducing or 
preventing number of students with mental health disorders (i.e., a positive screen for at 
least one of anxiety, depression, or ED as the outcome reflecting a "clinical 
case")assessed at 6 weeks, 6 months and 2 years, with a primary endpoint of 2 years. 
This allows for a single, universal outcome applicable to everyone in the study. Both 
logistic regression models will include random intercepts for colleges and for students 
nested within colleges, and account for repeated assessments within students, and will 
estimate the following fixed effects: main effect of time (coded as a factor variable, 
including baseline, 6-weeks, 6-months, and 2 years), treatment condition, and two-way 
interaction between time and treatment condition.  
 
To evaluate reduction in disorder-specific symptoms (Aim 2b) and improvement in 
quality of life (Aim 2c), multilevel models will be used. Analyses of symptom change will 
be conducted within each clinical disorder subgroup separately with relevant symptoms 
as the response variables. We will also use a z-score to standardize primary symptom 
measures to obtain an estimate for a unified intervention effect. All symptom models will 
include random intercepts for colleges and for students nested within colleges, account 
for repeated assessments within students, and will estimate the following fixed effects: 
main effect of time, treatment condition, and two-way interaction between time and 
treatment condition. Parameter estimates from these models will allow separately 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
21
comparing change between arms from pre to 6 weeks, pre to 6 months, and from pre to 
2-year follow-up. Each model will use dummy-coded factor terms as a stringent test of 
the treatment to ensure that: 1) treatment was associated with significantly greater 
change from pre to post than the control group; and 2) gains made in treatment at 6 
weeks are maintained and significantly greater than baseline at each follow-up. 
 
Analyses for Aim 3 (targets, other mediators, within-mobile program predictors, 
moderators). Potential mediators,are categorized into three groups: 1) clinical targets 
(Aim 3a and Aim 3b); 2) early engagement with services (Aim 3c); and 3) early clinical 
response/rapid response (Aim 3c). We will test for mediation using structural equation 
models (SEM) with confidence intervals derived from bootstrapping of indirect effects 
and total effects. We will examine targets and mediators preceding changes in 
outcomes. To draw more informative and coherent conclusions across the three 
disorders, we will explore meaningful transdiagnostic clinical targets as well as disorder 
specific targets. For transdiagnostic mediator outcomes, binary clinical case status and 
quality of life measures are natural outcome measures, but for symptom outcomes, we 
will use standardized symptom scores (for transdiagnostic outcomes) and 
nonstandardized symptoms scores (for disorder-specific outcomes). Moderated 
mediation (with treatment vs control as the moderator) will be tested by constraining the 
indirect effect to be equal across treatment and control groups and testing differences in 
chi-square fit. Any significant misfit caused by specifying the indirect effect of each 
mediator to be equal suggests that there are significant differences in this mediational 
path between the treatment and control. All mediational paths between the primary 
predictor and outcome will be estimated simultaneously to account for any and all 
interdependence between paths and to ensure these interdependencies do not bias 
estimation. Note that in applying moderated mediation analyses to both transdiagnostic 
and disorder-specific targets, we will be simultaneously testing whether the intervention 
engages the targets (Aim 3a) and whether intervention-induced changes in targets are 
associated with clinical benefit (Aim 3b). Putative moderators (Aim 3e) will examine 
interaction terms with time point and random assignment in regressions to identify 
subgroups that benefit most from treatment, and others for whom additional tailoring 
may be necessary in future refinements of the delivery model. In cases where 
moderators are significant, we will then conduct mediator analyses separately by 
subgroups defined by these moderators, because different intervention effects often 
imply different mechanisms by which effects are occurring216.In moderation models, 
results will estimate the following fixed effects: 1) main effect of time, treatment 
condition, and each proposed moderator; 2) two-way interactions between time and 
treatment condition, treatment condition and each moderator, and time and each 
moderator; and 3) three way interactions between time, treatment condition, and each 
moderator. The models will also estimate random effects of time nested within 
individuals, and intercepts nested within campuses. Within-mobile treatment predictors 
of change (Aim 3d) will use multilevel models using the fixed effects of maineffects of 
time, and each within-mobile program predictor, and the interaction between within-
mobile program predictors and time estimating random effect of time nested within 
individuals and intercepts nested within campuses. 
 
Analyses for Aim 4: Economic evaluation analyses (Aim 4a) were designed to provide 
information for stakeholders considering implementing the mobile platform217. As such, 
analyses will be conducted from the payer’s perspective to reflect opportunity cost for 
the payer. We will use TreeAge software, taking a multistep approach. Overall, the 
model will be evidence-based and fully validated using study data. A decision analytic 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
22
model using a microsimulation approach218 will be used to provide the most flexibility for 
testing model assumptions and provide clear evidence to decision-makers concerning 
reliability of results. Internal validation of the model will be conducted by comparing 
outcomes of participants whose data were used to construct the model to their predicted 
outcomes. Model fit will be tested using the R2 statistic for continuous data and the chi-
squared statistic for categorical data. External validity will be tested by comparing the 
model’s predicted outcome to actual for participants who were “excluded” from the 
model-building process. The outcome of interest is the net benefit of the intervention 
compared to usual care at 2 years. Sensitivity of the decision model to the assumptions, 
including time frame, discount rate, and perspective, will be thoroughly tested using 
deterministic and probabilistic sensitivity analyses.219,220 We will also calculate  
incremental cost-effectiveness ratios, corresponding to the incremental difference 
(intervention vs. control) in costs divided by the incremental difference in the primary 
outcome, the number of clinical cases at 2-year follow-up. Monte Carlo simulations and 
cost-effectiveness acceptability curves will be used to present uncertainty around this 
estimate. Academic outcomes (Aim 4b) will be analyzed using the methods described for 
the primary outcomes under Aim 1. Academic outcomes will also be included into cost 
analyses, by translating drop-outs from college into losses in tuition (for institutions) and 
losses in lifetime expected productivity. Stakeholder attitudes (Aim 4c) will be assessed 
for variation using descriptive statistics, and will be used to predict intention to adopt the 
platform using ordered probit regressions (with Likert categorical variables 
corresponding to intention to adopt). 
F4  Missing Outcome Data 
All analyses will be intent-to-treat analyses, using full-information maximum likelihood to 
handle missing data. 
 
G Data Handling and Record Keeping  
 
G1 Confidentiality and Security 
All surveys will be completed on the secured survey platform Qualtrics. Survey data will 
be directly downloaded by our Screening Implementation and Data Coordinating site, 
UM, onto a protected server.  
 
The data coordinating center will store the study data on secure, password-protected 
servers, with identifying information always kept separate from other study data (such as 
assessment responses). The data will only be accessible at any given time to the small 
number of study team members who need to use the data at that time. Data will be 
stored separately by participating institution until de-identified analytic files are 
assembled after data collection. 
 
Data will be transmitted betweem sites using the secured file share program Box.  
 
In order for the Fidelity Monitoring site, PSU, to review coach messages and data, 
SilverCloud Health will send PSU the data using Box. The data will be stored on 
password protected computers. 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
23
SilverCloud Health’s privacy is aligned with US personal data protection principles and is 
HIPAA compliant. 
 
All of our surveys will be completed using Qualtrics. Within Qualtrics we will create a 
contact list based on the registrar office database sent by the universities, which will 
allow Qualtrics to send out personalized email invitation to students.  Only our Data 
Coordinating Center, the University of Michigan, will have access to the identifying 
information and will create an anonymous study ID for each participant who agrees to 
participate in the study. With the assistance of SilverCloud we were able to create a 
code to automatically link the anonymous Qualtrics Response ID from the survey with 
the self-selected username in SilverCloud.  SilverCloud will only have access to limited 
data from Qualtrics for people who were randomized into the SilverCloud program. The 
data they will receive will help inform the coach on how to best support participants such 
as disorder risk level, suicide risk, gender, school, and co-morbidities. SilverCloud will 
also receive the email address of the participants in order to send them an automatic 
email to sign-up for the program.” 
G2 Training  
All staff personnel are trained and comply with HIPAA regulations. All study team 
members will complete the CITI training and Good Clinical Practice training. 
 
G3 Performance Monitoring  
The Data and Safety Monitoring Plan for this trial includes close monitoring by the 
principal investigators (PIs) and four independent safety officers through the DSMB.  
 
H Study Monitoring, Auditing, and Inspecting  
 
H1 Study Monitoring Plan  
 
Data Safety and Monitoring Board (DSMB) 
An independent panel of experts with experience in clinical trials, health services 
research, biostatistics, and college mental health, consisting of four members who are 
not affiliated with the study – including an intervention researcher, a counseling center 
director, a biostatistician, and a patient advocate – will be appointed to constitute a Data 
Safety and Monitoring Board (DSMB). Members will be named prior to the 
commencement of the study. In addition, the study PIs (Drs. Wilfley, Eisenberg, 
Newman, and Taylor) and designated staff will attend the DSMB meetings (as non-
voting participants) and will be responsible for preparing and presenting data reports 
from the study. The DSMB will provide oversight and ongoing monitoring of participant 
safety, quality of data collection, and integrity of the study on a yearly basis. The DSMB 
will receive a report from the study Screening Implementation and Data Coordinating 
Center approximately four weeks before each review date. These reports will include the 
major variables necessary for monitoring safety, quality of data collection, and integrity 
of the study and will include otherwise blinded outcome data. The DSMB will prepare a 
report after every meeting based on the material received from the Screening 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
24
Implementation and Data Coordinating Center, which will be forwarded to the PIs to 
review at Study Executive Committee meetings and also forwarded to the IRB of Record 
by Dr. Wilfley. 
 
The DSMB will provide the following functions: 
 
Review of data (including masked data) over the course of the trial relating to 
efficacy, recruitment, randomization, adherence, retention, operating procedures, 
forms completion, intervention effects, ethnic/racial minority inclusion, and 
participant safety. 
 
Identification of problems relating to safety over the course of the study. Study 
PIs and Program Administrators will be informed by phone and via written report 
of their findings and recommendations.  
 
Identification of needs for additional data relevant to safety issues and request of 
these data from the study investigators. 
 
Selection of appropriate analyses and periodic review of data on safety and 
outcomes. 
 
Recommendations regarding recruitment, treatment effects, adherence, 
retention, safety issues, and continuation of the study. 
 
Provision of written reports to the Project Officer and the PIs following each 
DSMB meeting. These reports will summarize the key issues reviewed by the 
DSMB. 
 
The frequency and type of data review is summarized in the following table, with 
additional information following: 
 
 
 
I Study Administration 
 
I1 
Organization and Participating Centers 
Overall responsibility for this project falls under PIs Denise E. Wilfley, PhD, Craig Barr 
Taylor, MD, Michelle Newman, PhD, and Daniel Eisenberg, PhD, continuing a long-
standing collaboration between them and their respective institutions. Each PI offers 
unique strengths to the proposed project. 
 
To conduct this trial, we propose a Clinical Coordinating Center, a Screening 
Implementation and Data Coordinating Center, a Fidelity Monitoring Center, a 
Technology Monitoring Center, and a Technology Partner, as well as a Study Executive 
Committee and expert consultants.  
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
25
Clinical Coordinating Center, headed by Dr. Wilfley, with the support of co-I Dr. 
Fitzsimmons-Craft at WUSM will be responsible for study oversight, including site 
recruitment, coordination and monitoring of study procedures and progress across sites, 
and collection of follow-up data.  
 
Screening Implementation and Data Coordinating Center, headed by Dr. Eisenberg 
at UCLA, with the support of co-Is Drs. Lipson, Justin Heinze, and Hyungjin Kim at UM, 
at well as  Dr. Newman at PSU, will be responsible for administering the online screen at 
each study site, identifying students who are eligible for the full study and overseeing 
referrals to the mobile platform or campus treatment options, data coordination and 
monitoring, and data analysis.  
 
Fidelity Monitoring Center, headed by Dr. Newman at PSU,  will ensure the integrity of 
coach training activities and fidelity monitoring of the coaching.  
 
Technology Partner SilverCloud Health will oversee the maintenance of the platform 
software.  
 
Technology Monitoring Center, headed by Dr. Taylor at PAU, will oversee the study’s 
use of technology, including monitoring and recording real-time technological 
improvements and ensuring that any platform changes (to keep pace with modern 
advances) sustain core intervention principles and follow proposed Consort standards.157 
This site will be the primary contact for SilverCloud Health and will ensure all 
withinprogram data are adequately labeled and tracked.  
 
Expert Consultation from Dr. Andrea Kass regarding cost analyses and mobile health 
delivery and evaluation, and from Dr. Helena Kraemer on methodological and statistical 
issues. 
I2 
Funding Source and Conflicts of Interest 
This study is funded by NIH. Any potential financial conflicts of interest for individual 
research team members are reported according to the site-specific IRB requirements 
and procedures. 
 
I3 
Committees 
Study Executive Committee will consist of PIs Drs. Wilfley, Eisenberg, Newman, 
Taylor, and co-Is Fitzsimmons-Craft, Lipson, and Kim. The PIs will be primarily 
responsible for decision-making related to the overall scientific conduct of the study and 
monitoring the overall study progress to ensure its timely completion, and the co-Is will 
assist in this process. Throughout the study, the committee will communicate weekly by 
teleconference to make study-related decisions. Daily communication about the project 
will be made by email and phone.  
I4 
Subject Stipends or Payments  
 
All students who receive a recruitment email will be automatically entered into a 
sweepstakes for one of 40 $100 electronic gift card prizes. 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
26
Participating universities and colleges will have the option to offer their own incentives to 
their students. The incentives offered by the school will be reviewed by the research 
team to make sure they are compliant with the IRB regulations, i.e., not coercive. 
 
Participants in the full study will be compensated by receiving an electronic gift card for 
completing the Baseline ($5), 6 week ($5), 6 month ($10), and 2-year ($20) follow-up 
assessments. Particpants will be entered into a sweepstakes to win one of five $50 gift 
cards after completing the Baseline, 6 week, 6 month, and 2-year surveys. In addition, 
once particpants complete all four surveys, they will be entered into a sweepstakes to 
win one of ten $100 gift cards. 
I5 
 Study Timetable 
 
The table below summarizes the timeline for the proposed five-year study. Each wave 
includes 5 campuses, for a total of 20 campuses across 4 waves. The waves are 
staggered by 6 months each, with the exception that the second wave will start a full 
year after the first wave to allow for careful adjustments in sampling (e.g., the size of the 
initial samples) if necessary. Data analysis and write-up will begin in month 43 so as to 
allow 18 months for these activities. 
 
 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
27
J Publication Plan  
 
 
Access to data is made equally available to the site PIs: Drs. Wilfley, Taylor, Newman, 
and Eisenberg and their teams. A publication plan will be formulated within the first two 
years of the trial to determine authorship order and responsibilities. Publication 
authorship will be based on the relative scientific contributions of the PIs, Co-Is, key 
personnel, and other members of the research team. 
 
 
 
 
 
 
 
 
 
 
K References 
 
1. Davies EB, Morriss R, Glazebrook C. Computer-delivered and web-based 
interventions to improve depression, anxiety, and psychological well-being of university 
students: A systematic review and metaanalysis. J Med Internet Res. 2014;16(5):e130. 
PMCID: PMC4051748 
2. Melioli T, Bauer S, Franko DL, et al. Reducing eating disorder symptoms and risk 
factors using the internet: A meta-analytic review. Int J Eat Disord. 2016;49(1):19-31. 
PMID: 26607683 
3. Hofman J, Pollitt A, Broeks M, Stewart K, van Stolk C. Review of computerised 
cognitive behavioural therapies: Products and outcomes for people with mental health 
needs. Rand Health Q. 2017;6(4):12. PMCID: PMC5627641 
4. Andersson G, Cuijpers P. Internet-based and other computerized psychological 
treatments for adult depression: a meta-analysis. Cogn Behav Ther. 2009;38(4):196-
205. PMID: 20183695 
5. Spek V, Cuijpers P, Nyklicek I, Riper H, Keyzer J, Pop V. Internet-based cognitive 
behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol 
Med. 2007;37(3):319-328. PMID: 17112400 
6. Kass AE, Trockel M, Safer DL, et al. Internet-based preventive intervention for 
reducing eating disorder risk: A randomized controlled trial comparing guided with 
unguided self-help. Behav Res Ther. 2014;63C:90-98. PMCID: PMC4383716 
7. Reetz DR, Krylowicz B, Bershad C, Lawrence JM, Mistler B. The Association for 
University and College Counseling Center Directors Annual Survey: Reporting period: 
September 1, 2014 through August 31, 2015. 2015. 
http://www.aucccd.org/assets/documents/aucccd%202015%20monograph%20- 
%20public%20version.pdf. 
8. Center for Collegiate Mental Health (CMMH). Center for Collegiate Mental Health: 
2016 Annual Report. 2016. 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
28
9. Twenge JM, Gentile B, DeWall CN, Ma D, Lacefield K, Schurtz DR. Birth cohort 
increases in psychopathology among young Americans, 1938–2007: A cross-temporal 
meta-analysis of the MMPI. Clin Psychol Rev. 2010;30(2):145-154. PMID: 19945203 
10. Kessler RC, Berglund P, Demler O, Jin R, Merikangas KR, Walters EE. Lifetime 
prevalence and age-ofonset distributions of DSM-IV disorders in the National 
Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):593-602. PMID: 
15939837 
11. Arria AM, Caldeira KM, Vincent KB, Winick ER, Baron RA, O'Grady KE. 
Discontinuous college enrollment: Associations with substance use and mental health. 
Psychiatr Serv. 2013;64(2):165-172. PMCID: PMC3609033 
12. De Hert M, Correll CU, Bobes J, et al. Physical illness in patients with severe mental 
disorders. I. Prevalence, impact of medications and disparities in health care. World 
Psychiatry. 2011;10(1):52-77. PMCID: PMC3048500 
13. Kafali N, Cook B, Wang S, Martinez PG, Selke Z, Blanco C. Impact of mental 
disorders on employment and receipt of public assistance: An instrumental variables 
approach. J Ment Health Policy Econ. 2015;18(3):137-145. PMID: 26474049 
14. Kessler RC, Foster CL, Saunders WB, Stang PE. Social consequences of 
psychiatric disorders, I: Educational attainment. Am J Psychiatry. 1995;152(7):1026-
1032. PMID: 7793438 
15. Kessler RC, Walters EE, Forthofer MS. The social consequences of psychiatric 
disorders, III: Probability of marital stability. Am J Psychiatry. 1998;155(8):1092-1096. 
PMID: 9699699 
16. Blanco C, Okuda M, Wright C, et al. Mental health of college students and their non-
college-attending peers: Results from the National Epidemiologic Study on Alcohol and 
Related Conditions. Arch Gen Psychiatry. 2008;65(12):1429-1437. PMCID: 
PMC2734947 
17. Eisenberg D, Hunt J, Speer N. Mental health in American colleges and universities: 
Variation across student subgroups and across campuses. J Nerv Ment Dis. 
2013;201(1):60-67. PMID: 23274298 
18. Auerbach RP, Alonso J, Axinn WG, et al. Mental disorders among college students 
in the World Health Organization World Mental Health Surveys. Psychol Med. 
2016;46(14):2955-2970. PMCID: PMC5129654 
19. Zivin K, Eisenberg D, Gollust SE, Golberstein E. Persistence of mental health 
problems and needs in a college student population. J Affect Disord. 2009;117(3):180-
185. PMID: 19178949 
20. National Institute of Mental Health. Strategic Research Priorities Overview. 2017. 
https://www.nimh.nih.gov/about/strategic-planning-reports/strategic-research-
priorities/index.shtml. Accessed 10/6/2017. 
21. Eisenberg D, Nicklett EJ, Roeder K, Kirz NE. Eating disorder symptoms among 
college students: Prevalence, persistence, correlates, and treatment-seeking. J Am Coll 
Health. 2011;59(8):700-707. PMCID: PMC3721327 
22. Eisenberg D, Hunt J, Speer N, Zivin K. Mental health service utilization among 
college students in the United States. J Nerv Ment Dis. 2011;199(5):301-308. PMID: 
21543948 
23. Aud S, KewalRamani A, Frohlich L. America's youth: Transitions to adulthood. 
National Center for Education Statistics. 2012. 
http://ies.ed.gov/pubsearch/pubsinfo.asp?pubid=2012026 
24. Kazdin AE. Addressing the treatment gap: A key challenge for extending evidence-
based psychosocial interventions. Behav ResTher. 2017;88:7-18. PMID: 28110678 
25. Kazdin AE, Fitzsimmons-Craft EE, Wilfley DE. Addressing critical gaps in the 
treatment of eating disorders. Int J Eat Disord. 2017:n/a-n/a. PMID: 28102908 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
29
26. Michaud CM, McKenna MT, Begg S, et al. The burden of disease and injury in the 
United States 1996. Popul Health Metr. 2006;4:11. PMCID: PMC1635736 
27. Wang PS, Berglund P, Olfson M, Pincus HA, Wells KB, Kessler RC. Failure and 
delay in initial treatment contact after first onset of mental disorders in the National 
Comorbidity Survey Replication. Arch Gen Psychiatry. 2005;62(6):603-613. PMID: 
15939838 
28. Gelenberg AJ. The prevalence and impact of depression. J Clin Psychiatry. 
2010;71(3):e06. PMID: 20331925 
29. Yonkers KA, Bruce SE, Dyck IR, Keller MB. Chronicity, relapse, and illness--course 
of panic disorder, social phobia, and generalized anxiety disorder: findings in men and 
women from 8 years of follow-up. Depress Anxiety. 2003;17(3):173-179. PMID: 
12768651 
30. Hargreaves DS, Greaves F, Levay C, et al. Comparison of health care experience 
and access between young and older adults in 11 high-income countries. J Adolesc 
Health. 2015;57(4):413-420. PMID: 26205758 
31. Ettner SL, Frank RG, Kessler RC. The impact of psychiatric disorders on labor 
market outcomes. ILR Review. 1997;51(1):64-81. 
32. Eisenberg D, Speer N, Hunt JB. Attitudes and beliefs about treatment among college 
students with untreated mental health problems. Psychiatr Serv. 2012;63(7):711-713. 
PMID: 22752036 33. Riley WT, Nilsen WJ, Manolio TA, Masys DR, Lauer M. News from 
the NIH: Potential contributions of the behavioral and social sciences to the precision 
medicine initiative. Transl Behav Med. 2015;5(3):243-246. PMCID: PMC4537462 
34. National Institutes of Health. NIH-Wide Strategic Plan, Fiscal Years 2016-2020: 
Turning Discovery into Health. Bethesda, MD: National Institutes of Health; 2015. 
35. Kumar S, Nilsen WJ, Abernethy A, et al. Mobile health technology evaluation: the 
mHealth evidence workshop. Am J Prev Med. 2013;45(2):228-236. PMCID: 
PMC3803146 
36. American College Health Association. American College Health Association-National 
College Health Assessment II: Reference Group Executive Summary Spring 2015. 
Hanover, MD: American College Health Association; 2015. 
37. Jones M, Kass AE, Trockel M, Glass AI, Wilfley DE, Taylor CB. A population-wide 
screening and tailored intervention platform for eating disorders on college campuses: 
the healthy body image program. J Am Coll Health. 2014;62(5):351-356. PMCID: 
PMC4031301 
38. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the 
anxiety and depressive disorders is effective, acceptable and practical health care: a 
meta-analysis. PLoS One. 2010;5(10):e13196. PMCID: PMC2954140 
39. Harris poll. Pearson student mobile device survey 2015: National report: College 
students. 2015. http://www.pearsoned.com/wp-content/uploads/2015-Pearson-Student-
Mobile-Device-Survey- College.pdf. 
40. Kanuri N, Taylor CB, Cohen JM, Newman MG. Classification models for 
subthreshold generalized anxiety disorder in a college population: Implications for 
prevention. J Anxiety Disord. 2015;34:43-52. PMID: 26119139 
41. Kass AE, Balantekin KN, Fitzsimmons-Craft EE, Jacobi C, Wilfley DE, Taylor CB. 
The economic case for digital interventions for eating disorders among United States 
college students. Int J Eat Disord. 2017;50(3):250-258. PMCID: PMC5391044 
42. Munoz RF. Using evidence-based internet interventions to reduce health disparities 
worldwide. J Med Internet Res. 2010;12(5):e60. PMCID: PMC3057307 
43. Mohr DC, Cheung K, Schueller SM, Hendricks Brown C, Duan N. Continuous 
evaluation of evolving behavioral intervention technologies. Am Journal Prev Med. 
2013;45(4):517-523. PMCID: PMC3828034 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
30
44. Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in 
college. B E J of Econom Anal Policy. 2009;9(1):Article 40. 
45. Lipson SK, Jones JM, Taylor CB, et al. Understanding and promoting treatment-
seeking for eating disorders and body image concerns on college campuses through 
online screening, prevention and intervention. Eat Behav. 2016. PMID: 27117825 
46. Kanuri N, Cohen JM, Jones M, Ruzek J, Taylor CB, Newman MG. Coached self-
help: Notes from the field. In M. Moessner (Chair). Moving beyond efficacy: Investigation 
of settings, processes, and training in Internet based interventions. 45th Annual Meeting 
of the Society for Psychotherapy Research; June, 2015; Philadelphia, PA. 
47. Newman MG, Kenardy J, Herman S, Taylor CB. Comparison of palmtop-computer-
assisted brief cognitive-behavioral treatment to cognitive-behavioral treatment for panic 
disorder. J Consult Clin Psychol. 1997;65(1):178-183. PMID: 9103747 
48. Kenardy JA, Dow MGT, Johnston DW, Newman MG, Thomson A, Taylor CB. A 
comparison of delivery methods of cognitive-behavioral therapy for panic disorder: An 
international multicenter trial. J Consult Clin Psychology. 2003;71(6):1068-1075. PMID: 
14622082 
49. Newman MG, Przeworski A, Consoli AJ, Taylor CB. A randomized controlled trial of 
ecological momentary intervention plus brief group therapy for generalized anxiety 
disorder. Psychotherapy (Chic). 2014;51(2):198-206. PMCID: PMC4440457 
50. Gruber K, Moran PJ, Roth WT, Taylor CB. Computer-assisted cognitive behavioral 
group therapy for social phobia. Behav Ther. 2001;32(1):155-165. 
51. Dow MGT, Kenardy JA, Johnston DW, Newman MG, Taylor CB, Thomson A. 
Prognostic indices with brief and standard CBT for panic disorder: II. Moderators of 
outcome. Psychol Medicine. 2007;37(10):1503-1509. PMID: 17493295 
52. Kanuri N, Newman MG, Ruzek JI, et al. The feasibility, acceptability, and efficacy of 
delivering Internetbased self-help and guided self-help interventions for generalized 
anxiety disorder to Indian university students: Design of a randomized controlled trial. 
JMIR Res Protoc. 2015;4(4):e136. PMCID: PMC4704912 
53. Kanuri N, Newman MG, Ruzek JI, et al. Implementing a stepped-care model of 
digital mental health interventions in Indian universities: The role of engagement in 
development. Eighth Scientific Meeting of the International Society for Research on 
Internet Interventions; April, 2016; Seattle, Washington. 
54. LaFreniere LS, Newman MG. A brief ecological momentary intervention for 
generalized anxiety disorder: A randomized controlled trial of the worry outcome journal. 
Depress Anxiety. 2016;33(9):829- 839. PMID: 27062682 
55. Newman MG, Kanuri N, Ruzek JI, et al. A randomized controlled trial of feasibility, 
acceptability, and efficacy of online, CBT-based guided self-help programs to reduce 
anxiety among students in India. 36th Annual Convention of the Anxiety and Depression 
Association of America; March, 2016; Philadelphia, PA. 
56. Newman MG, Szkodny LE, Llera SJ, Przeworski A. A review of technology-assisted 
self-help and minimal contact therapies for anxiety and depression: Is human contact 
necessary for therapeutic efficacy? Clin Psychol Rev. 2011;31(1):89-103. PMID: 
21130939 
57. Hunkeler EM, Hargreaves WA, Fireman B, et al. A web-delivered care management 
and patient selfmanagement program for recurrent depression: A randomized trial. 
Psychiatr Serv. 2012;63(11):1063- 1071. PMID: 22983558 
58. O'Neil A, Taylor B, Sanderson K, et al. Efficacy and feasibility of a tele-health 
intervention for acute coronary syndrome patients with depression: results of the 
"MoodCare" randomized controlled trial. Ann Behav Med. 2014;48(2):163-174. PMID: 
24570217 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
31
59. Trockel M, Manber R, Chang V, Thurston A, Taylor CB. An e-mail delivered CBT for 
sleep-health program for college students: effects on sleep quality and depression 
symptoms. J Clin Sleep Med. 2011;7(3):276-281. PMCID: PMC3113967 
60. Redzic NM, Taylor K, Chang V, Trockel M, Shorter A, Taylor CB. An Internet-based 
positive psychology program: Strategies to improve effectiveness and engagement. The 
Journal of Positive Psychology. 2014;9(6):494-501. 
61. Taylor CB, Bryson S, Luce KH, et al. Prevention of eating disorders in at-risk college-
age women. Arch General Psychiatry. 2006;63(8):881-888. PMCID: PMC3837629 
62. Taylor CB, Kass AE, Trockel M, et al. Reducing eating disorder onset in a very high 
risk sample with significant comorbid depression: A randomized controlled trial. J 
Consult Clin Psychol. 2016;84(5):402- 414. PMCID: PMC4836995 
63. Wilfley DE, Agras WS, Taylor CB. Reducing the burden of eating disorders: A model 
for populationbased prevention and treatment for university and college campuses. Int J 
Eat Disord. 2013;46(5):529- 532. PMCID: PMC3703768 
64. Jones M, Luce KH, Osborne MI, et al. Randomized, controlled trial of an Internet-
facilitated intervention for reducing binge eating and overweight in adolescents. 
Pediatrics. 2008;121(3):453462. PMID: 18310192 
65. Völker U, Jacobi C, Taylor CB. Adaptation and evaluation of an Internet-based 
prevention program for eating disorders in a sample of women with subclinical eating 
disorder symptoms: A pilot study. Eat Weight Disord. 2011;16(4):e270-e273. PMID: 
22526132 
66. Saekow J, Jones M, Gibbs E, et al. StudentBodies-eating disorders: A randomized 
controlled trial of a coached online intervention for subclinical eating disorders. Internet 
Interv. 2015;2(4):419-428. 
67. Wilfley DE, Welch RR, Stein RI, et al. A randomized comparison of group cognitive-
behavioral therapy and group interpersonal psychotherapy for the treatment of 
overweight individuals with binge-eating disorder. Arch Gen Psychiatry. 2002;59(8). 
PMID: 12150647 
68. Wilson GT, Wilfley DE, Agras WS, Bryson SW. Psychological treatments of binge 
eating disorder. Arch Gen Psychiatry. 2010;67(1):94-101. PMCID: PMC3757519 
69. Borkovec TD, Newman MG, Pincus AL, Lytle R. A component analysis of cognitive-
behavioral therapy for generalized anxiety disorder and the role of interpersonal 
problems. J Consult Clin Psychol. 2002;70(2):288-298. PMID: 11952187 
70. Newman MG, Przeworski A, Fisher AJ, Borkovec TD. Diagnostic comorbidity in 
adults with generalized anxiety disorder: Impact of comorbidity on psychotherapy 
outcome and impact of psychotherapy on comorbid diagnoses. Behav Ther. 
2010;41(1):59-72. PMCID: PMC2827339 
71. Newman MG, Castonguay LG, Borkovec TD, et al. A randomized controlled trial of 
cognitive-behavioral therapy for generalized anxiety disorder with integrated techniques 
from emotion-focused and interpersonal therapies. J Consult Clin Psychol. 
2011;79(2):171-181. PMCID: PMC3078794 
72. Brown GK, Karlin BE, Trockel M, Gordienko M, Yesavage J, Taylor CB. 
Effectiveness of cognitive behavioral therapy for veterans with depression and suicidal 
ideation. Arch Suicide Res. 2016;20(4):677-682. PMID: 26983897 
73. Karlin BE, Trockel M, Brown GK, Gordienko M, Yesavage J, Taylor CB. Comparison 
of the effectiveness of cognitive behavioral therapy for depression among older versus 
younger veterans: results of a national evaluation. J Gerontol B Psychol Sci Soc Sci. 
2015;70(1):3-12. PMID: 24218096 
74. Strachowski D, Khaylis A, Conrad A, Neri E, Spiegel D, Taylor CB. The effects of 
cognitive behavior therapy on depression in older patients with cardiovascular risk. 
Depress Anxiety. 2008;25(8):E1-10. PMID: 17377961 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
32
75. Eisenberg D, Hunt J, Speer N. Help seeking for mental health on college campuses: 
review of evidence and next steps for research and practice. Harv Rev Psychiatry. 
2012;20(4):222-232. PMID: 22894731 76. Killen JD, Taylor CB, Hayward C, et al. 
Pursuit of thinness and onset of eating disorder symptoms in a community sample of 
adolescent girls: A three-year prospective analysis. Int J Eat Disord. 1994;16(3):227-
238. PMID: 7833956 
77. Killen JD, Taylor CB, Hayward C, et al. Weight concerns influence the development 
of eating disorders: A 4-year prospective study. J Consult Clin Psychol. 1996;64(5):936-
940. PMID: 8916622  
78. Jacobi C, Fittig E, Bryson SW, Wilfley D, Kraemer HC, Taylor CB. Who is really at 
risk? Identifying risk factors for subthreshold and full syndrome eating disorders in a 
high-risk sample. Psychol Med. 2011;41(9):1939-1949. PMCID: PMC3782385 
79. Ohlmer R, Jacobi C, Taylor CB. Preventing symptom progression in women at risk 
for AN: Results of a pilot study. Eur Eat Disord Rev. 2013;21(4):323-329. PMID: 
23520152 
80. Jacobi C, Völker U, Trockel MT, Taylor CB. Effects of an internet-based intervention 
for subthreshold eating disorders: A randomized controlled trial. Behav Res Ther. 
2012;50(2):93-99. PMID: 22137366 
81. Ruscio AM, Chiu WT, Roy-Byrne P, et al. Broadening the definition of generalized 
anxiety disorder: Effects on prevalence and associations with other disorders in the 
National Comorbidity Survey Replication. J Anxiety Disord. 2007;21(5):662-676. PMCID: 
PMC2475335 
82. Hayward C, Killen JD, Kraemer HC, Taylor CB. Linking self-reported childhood 
behavioral inhibition to adolescent social phobia. J Am Acad Child Adolesc Psychiatry. 
1998;37(12):1308-1316. PMID: 9847504 
83. Hayward C, Killen JD, Kraemer HC, Taylor CB. Predictors of panic attacks in 
adolescents. J Am Acad Child Adolesc Psychiatry. 2000;39(2):207-214. PMID: 
10673832 
84. Karsten J, Hartman CA, Smit JH, et al. Psychiatric history and subthreshold 
symptoms as predictors of the occurrence of depressive or anxiety disorder within 2 
years. Br J Psychiatry. 2011;198(3):206-212. PMID: 21357879 
85. Karsten J, Penninx BWJH, Verboom CE, Nolen WA, Hartman CA. Course and risk 
factors of functional impairment in subthreshold depression and anxiety. Depress 
Anxiety. 2013;30(4):386-394. PMID: 23165799 
86. Shankman SA, Lewinsohn PM, Klein DN, Small JW, Seeley JR, Altman SE. 
Subthreshold conditions as precursors for full syndrome disorders: A 15-year 
longitudinal study of multiple diagnostic classes. J 
Child Psychol Psychiatry. 2009;50(12):1485-1494. PMCID: PMC2804772 
87. American Psychological Association. Clinical implications of "subthreshold" 
depressive symptoms. 2000. 
88. Fergusson DM, Horwood L, Ridder EM, Beautrais AL. Subthreshold depression in 
adolescence and mental health outcomes in adulthood. Arch Gen Psychiatry. 
2005;62(1):66-72. PMID: 15630074 
89. Cuijpers P, Smit F. Subthreshold depression as a risk indicator for major depressive 
disorder: A systematic review of prospective studies. Acta Psychiatr Scand. 
2004;109(5):325-331. PMID: 15049768 
90. Asselmann E, Wittchen H-U, Lieb R, Höfler M, Beesdo-Baum K. Associations of 
fearful spells and panic attacks with incident anxiety, depressive, and substance use 
disorders: A 10-year prospectivelongitudinal community study of adolescents and young 
adults. J Psychiatr Res. 2014;55:8-14. PMID: 24774646 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
33
91. Angst J, Merikangas K. The depressive spectrum: Diagnostic classification and 
course. J Affect Disord. 1997;45(1-2):31-40. PMID: 9268773 
92. Goodwin RD, Lieb R, Hoefler M, et al. Panic attack as a risk factor for severe 
psychopathology. Am J Psychiatry. 2004;161(12):2207-2214. PMID: 15569891 
93. Newman MG, Kanuri N, Ruzek JI, et al. A randomized controlled trial of feasibility, 
acceptability, and efficacy of online, CBT-based guided self-help programs to reduce 
anxiety among students in India. 36th Annual Convention of the Anxiety and Depression 
Association of America; March, 2016; Philadelphia, PA. 
94. Richards D, Richardson T. Computer-based psychological treatments for 
depression: a systematic review and meta-analysis. Clin Psychol Rev. 2012;32(4):329-
342. PMID: 22466510 
95. Durham RC, Allan T, Hackett CA. On predicting improvement and relapse in 
generalized anxiety disorder following psychotherapy. Br J Clin Psychol. 1997;36(1):101-
119. PMID: 9051282 
96. Lutz W, Hofmann SG, Rubel J, et al. Patterns of early change and their relationship 
to outcome and early treatment termination in patients with panic disorder. J Consult Clin 
Psychol. 2014;82(2):287-297. PMCID: PMC3966935 
97. Lewis CC, Simons AD, Kim HK. The role of early symptom trajectories and 
pretreatment variables in predicting treatment response to cognitive behavioral therapy. 
J Consult Clin Psychol. 2012;80(4):525- 534. PMID: 22730951 
98. Moscovitch DA, Gavric DL, Senn JM, et al. Changes in judgment biases and use of 
emotion regulation strategies during cognitive-behavioral therapy for social anxiety 
disorder: Distinguishing treatment responders from nonresponders. Cognit Ther Res. 
2012;36(4):261-271. 
99. Bradford A, Cully J, Rhoades H, et al. Early response to psychotherapy and long-
term change in worry symptoms in older adults with generalized anxiety disorder. Am J 
Geriatr Psychiatry. 2011;19(4):347- 356. PMCID: PMC3058752 
100. Munsch S, Meyer AH, Biedert E. Efficacy and predictors of long-term treatment 
success for cognitivebehavioral treatment and behavioral weight-loss-treatment in 
overweight individuals with binge eating disorder. Behav Res Ther. 2012;50(12):775-
785. PMID: 23099111 
101. Vall E, Wade TD. Predictors of treatment outcome in individuals with eating 
disorders: A systematic review and meta-analysis. Int J Eat Disord. 2015;48(7):946-971. 
PMID: 26171853 
102. Wilson GT, Fairburn CC, Agras WS, Walsh BT, Kraemer H. Cognitive-behavioral 
therapy for bulimia nervosa: Time course and mechanisms of change. J Consult Clin 
Psychol. 2002;70(2):267-274. PMID: 11952185 
103. Buntrock C, Ebert D, Lehr D, et al. Effectiveness of a web-based cognitive 
behavioural intervention for subthreshold depression: Pragmatic randomised controlled 
trial. Psychother Psychosom. 2015;84(6):348-358. PMID: 26398885 
104. Imamura K, Kawakami N, Furukawa TA, et al. Does Internet-based cognitive 
behavioral therapy (iCBT) prevent major depressive episode for workers? A 12-month 
follow-up of a randomized controlled trial. Psychol Med. 2015;45(9):1907-1917. PMID: 
25562115 
105. Thompson NJ, Patel AH, Selwa LM, et al. Expanding the efficacy of Project 
UPLIFT: Distance delivery of mindfulness-based depression prevention to people with 
epilepsy. J Consult Clin Psychol. 2015;83(2):304-313. PMCID: PMC4380523 
106. American Medical Association. Stepped-care prevention of anxiety and depression 
in late life: A randomized controlled trial. 2009. 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
34
107. Titov N, Andrews G, Davies M, McIntyre K, Robinson E, Solley K. Internet 
treatment for depression: a randomized controlled trial comparing clinician vs. technician 
assistance. PLoS One. 2010;5(6):e10939. PMCID: PMC2882336 
108. Robinson E, Titov N, Andrews G, McIntyre K, Schwencke G, Solley K. Internet 
treatment for generalized anxiety disorder: A randomized controlled trial comparing 
clinician vs. technician assistance. PLoS One. 2010;5(6):e10942. PMCID: PMC2880592 
109. Johansson R, Sjoberg E, Sjogren M, et al. Tailored vs. standardized internet-based 
cognitive behavior therapy for depression and comorbid symptoms: a randomized 
controlled trial. PLoS One. 2012;7(5):e36905. PMCID: PMC3352859 
110. Titov N, Andrews G, Johnston L, Robinson E, Spence J. Transdiagnostic internet 
treatment for anxiety disorders: A randomized controlled trial. Behav ResTher. 
2010;48(9):890-899. PMID: 20561606 
111. Carrard I, Crépin C, Rouget P, Lam T, Golay A, Van der Linden M. Randomised 
controlled trial of a guided self-help treatment on the Internet for binge eating disorder. 
Behav Res Ther. 2011;49(8):482- 491. PMID: 21641580 
112. Ruwaard J, Lange A, Broeksteeg J, et al. Online cognitive–behavioural treatment of 
bulimic symptoms: A randomized controlled trial. Clin Psychol Psychother. 
2013;20(4):308-318. PMID: 22298417 
113. Smits JA, Julian K, Rosenfield D, Powers MB. Threat reappraisal as a mediator of 
symptom change in cognitive-behavioral treatment of anxiety disorders: a systematic 
review. J Consult Clin Psychol. 2012;80(4):624-635. PMCID: PMC3404225 
114. Lemmens LHJM, Müller VNLS, Arntz A, Huibers MJH. Mechanisms of change in 
psychotherapy for depression: An empirical update and evaluation of research aimed at 
identifying psychological mediators. Clin Psychol Rev. 2016;50:95-107. PMID: 27770716 
115. Linardon J, de la Piedad Garcia X, Brennan L. Predictors, moderators, and 
mediators of treatment outcome following manualised cognitive-behavioural therapy for 
eating disorders: A systematic review. Eur Eat Disord Rev. 2017;25(1):3-12. PMID: 
27862611 
116. Fairburn CG. Cognitive Behavior Therapy and Eating Disorders. New York, NY: 
The Guilford Press; 2008. 
117. Hundt NE, Williams AM, Mendelson J, Nelson-Gray RO. Coping mediates 
relationships between reinforcement sensitivity and symptoms of psychopathology. 
Personality and Individual Differences. 2013;54(6):726-731. 
118. Hundt NE, Calleo JS, Williams W, Cully JA. Does using cognitive-behavioural 
therapy skills predict improvements in depression? Psychol Psychother. 2016;89(2):235-
238. PMID: 26173639 
119. Jacob KL, Christopher MS, Neuhaus EC. Development and validation of the 
cognitive-behavioral therapy skills questionnaire. Behav Modif. 2011;35(6):595-618. 
PMID: 21893554 
120. Meuret AE, Wolitzky-Taylor KB, Twohig MP, Craske MG. Coping Skills and 
Exposure Therapy in Panic Disorder and Agoraphobia: Latest Advances and Future 
Directions. Behavior Therapy. 2012;43(0):271-284. PMCID: PMC3327306 
121. Webb CA, Beard C, Kertz SJ, Hsu KJ, Björgvinsson T. Differential role of CBT 
skills, DBT skills and psychological flexibility in predicting depressive versus anxiety 
symptom improvement. Beh ResTher. 2016;81:12-20. PMCID: PMC4860036 
122. Schmidt NB, Woolaway Bickel K. The effects of treatment compliance on outcome 
in cognitivebehavioral therapy for panic disorder: Quality versus quantity. J Consul Clin 
Psychol. 2000;68(1):13-18. PMID: 10710836 
123. Cuming S, Rapee RM, Kemp N, Abbott MJ, Peters L, Gaston JE. A self-report 
measure of subtle avoidance and safety behaviors relevant to social anxiety: 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
35
Development and psychometric properties. J Anxiety Disord. 2009;23(7):879-883. PMID: 
19556098  
124. Chu BC, Harrison TL. Disorder-specific effects of CBT for anxious and depressed 
youth: A metaanalysis of candidate mediators of change. Clin Child Fam Psychol Rev. 
2007;10(4):352-372. PMID: 17985239 
125. Aderka IM, McLean CP, Huppert JD, Davidson JRT, Foa EB. Fear, avoidance and 
physiological symptoms during cognitive-behavioral therapy for social anxiety disorder. 
Behav Res Ther. 2013;51(7):352-358. PMCID: PMC3669252 
126. Bewernick BH, Urbach AS, Broder A, Kayser S, Schlaepfer TE. Walking away from 
depression-motor activity increases ratings of mood and incentive drive in patients with 
major depression. Psychiatry Res. 2017;247:68-72. PMID: 27865100 
127. Ryba MM, Lejuez CW, Hopko DR. Behavioral activation for depressed breast 
cancer patients: the impact of therapeutic compliance and quantity of activities 
completed on symptom reduction. J Consult Clin Psychol. 2014;82(2):325-335. PMID: 
24364801 
128. Manos RC, Kanter JW, Luo W. The Behavioral Activation for Depression Scale–
Short Form: Development and Validation. Behav Ther. 2011;42(4):726-739. PMID: 
22036000 
129. Spangler DL, Baldwin SA, Agras WS. An examination of the mechanisms of action 
in cognitive behavioral therapy for bulimia nervosa. BehavTher. 2004;35(3):537-560. 
130. Sharry J, Davidson R, McLoughlin O, Doherty G. A service-based evaluation of a 
therapist-supported online cognitive behavioral therapy program for depression. J Med 
Internet Res. 2013;15(6):e121. PMCID: PMC3713925 
131. Erekson DM, Horner J, Lambert MJ. Different lens or different picture? Comparing 
methods of defining dramatic change in psychotherapy. Psychother Res. 2016:1-11. 
132. Lambert MJ. Early response in psychotherapy: further evidence for the importance 
of common factors rather than "placebo effects". Journal of clinical psychology. 
2005;61(7):855-869. PMID: 15827996 
133. Aaronson CJ, Shear MK, Goetz RR, et al. Predictors and time course of response 
among panic disorder patients treated with cognitive-behavioral therapy. J Clin 
Psychiatry. 2008;69(3):418-424. PMID: 18278989 
134. Aderka IM, Nickerson A, Boe HJ, Hofmann SG. Sudden gains during psychological 
treatments of anxiety and depression: a meta-analysis. J Consult Clin Psychol. 
2012;80(1):93-101. PMID: 22122290 
135. Bohn C, Aderka IM, Schreiber F, Stangier U, Hofmann SG. Sudden gains in 
cognitive therapy and interpersonal therapy for social anxiety disorder. J Consult Clin 
Psychol. 2013;81(1):177-182. PMID: 23244366 
136. Norton PJ, Klenck SC, Barrera TL. Sudden gains during cognitive-behavioral group 
therapy for anxiety disorders. J Anxiety Disord. 2010;24(8):887-892. PMCID: 
PMC2956855 
137. Lutz W, Stulz N, Kock K. Patterns of early change and their relationship to outcome 
and follow-up among patients with major depressive disorders. J Affect Disord. 
2009;118(1-3):60-68. PMID: 19217669 
138. Tang TZ, DeRubeis RJ. Sudden gains and critical sessions in cognitive-behavioral 
therapy for depression. J Consult Clin Psychol. 1999;67(6):894-904. PMID: 10596511 
139. Borkovec TD, Nau SD. Credibility of analogue therapy rationales. J Behav Ther Exp 
Psychiatry. 1972;3(4):257-260. 
140. Borkovec TD, Sides JK. The contribution of relaxation and expectancy to fear 
reduction via graded, imaginal exposure to feared stimuli. Behav Res Ther. 
1979;17(6):529-540. PMID: 43126 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
36
141. Brown LA, Wiley JF, Wolitzky-Taylor K, et al. Changes in self-efficacy and outcome 
expectancy as predictors of anxiety outcomes from the CALM study. Depress Anxiety. 
2014;31(8):678-689. PMCID: PMC4244230 
142. Curry J, Rohde P, Simons A, et al. Predictors and moderators of acute outcome in 
the Treatment for Adolescents with Depression Study (TADS). J Am Acad Child Adolesc 
Psychiatry. 2006;45(12):1427- 1439. PMID: 17135988 
143. Westra HA, Dozois DJ, Marcus M. Expectancy, homework compliance, and initial 
change in cognitivebehavioral therapy for anxiety. J Consult Clin Psychol. 
2007;75(3):363-373. PMID: 17563153 
144. Brown TA, Barlow DH. Long-term outcome in cognitive-behavioral treatment of 
panic disorder: clinical predictors and alternative strategies for assessment. J Consult 
Clin Psychol. 1995;63(5):754-765. PMID: 7593868 
145. Dow MG, Kenardy JA, Johnston DW, Newman MG, Taylor CB, Thomson A. 
Prognostic indices with brief and standard CBT for panic disorder: II. Moderators of 
outcome. Psychol Med. 2007;37(10):1503- 1509. PMID: 17493295 
146. Hilbert A, Saelens BE, Stein RI, et al. Pretreatment and process predictors of 
outcome in interpersonal and cognitive behavioral psychotherapy for binge eating 
disorder. J Consult Clin Psychol. 2007;75(4):645-651. PMID: 17663618 
147. Lelliott PT, Marks IM, Monteiro WO, Tsakiris F, Noshirvani H. Agoraphobics 5 years 
after imipramine and exposure. Outcome and predictors. J Nerv Ment Dis. 
1987;175(10):599-605. PMID: 3309186 
148. Basoglu M, Marks IM, Swinson RP, Noshirvani H, O'Sullivan G, Kuch K. Pre-
treatment predictors of treatment outcome in panic disorder and agoraphobia treated 
with alprazolam and exposure. J Affect Disord. 1994;30(2):123-132. PMID: 7911132 
149. Dow MGT, Kenardy JA, Johnston DW, Newman MG, Taylor CB, Thomson A. 
Prognostic indices with brief and standard CBT for panic disorder: I. Predictors of 
outcome. Psychol Med. 2007;37(10):1493- 1502. PMID: 17493294 
150. Newman MG, Fisher AJ. Mediated moderation in combined cognitive behavioral 
therapy versus component treatments for generalized anxiety disorder. J Consult Clin 
Psychol. 2013;81(3):405-414. PMCID: PMC4432847 
151. Heldt E, Manfro GG, Kipper L, et al. Treating medication-resistant panic disorder: 
predictors and outcome of cognitive-behavior therapy in a Brazilian public hospital. 
Psychother Psychosom. 2003;72(1):43-48. PMID: 12466637 
152. Newman MG, Przeworski A, Fisher AJ, Borkovec TD. Diagnostic comorbidity in 
adults with generalized anxiety disorder: impact of comorbidity on psychotherapy 
outcome and impact of psychotherapy on comorbid diagnoses. Behav Ther. 
2010;41(1):59-72. PMCID: PMC2827339 
153. Scheibe G, Albus M. Predictors of outcome in panic disorder: a 5-year prospective 
follow-up study. J Affect Disord. 1996;41(2):111-116. PMID: 8961038 
154. Tsao JC, Lewin MR, Craske MG. The effects of cognitive-behavior therapy for 
panic disorder on comorbid conditions. J Anxiety Disord. 1998;12(4):357-371. PMID: 
9699119 
155. Westra HA. Comparing the predictive capacity of observed in-session resistance to 
self-reported motivation in cognitive behavioral therapy. Behav Res Ther. 
2011;49(2):106-113. PMCID: PMC3037024 
156. Versluis A, Verkuil B, Spinhoven P, van der Ploeg MM, Brosschot JF. Changing 
mental health and positive psychological well-being using ecological momentary 
interventions: A systematic review and meta-analysis. J Med Internet Res. 
2016;18(6):e152. PMCID: PMC4940607 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
37
157. Eysenbach G. CONSORT-EHEALTH: Implementation of a checklist for authors and 
editors to improve reporting of web-based and mobile randomized controlled trials. Stud 
Health Technol Inform. 2013;192:657-661. PMID: 23920638 
158. Lipson SK, Speer N, Brunwasser S, Hahn E, Eisenberg D. Gatekeeper training and 
access to mental health care at universities and colleges. J Adolesc Health. 
2014;55(5):612-619. PMCID: PMC4209329 
159. Eisenberg D, Golberstein E, Whitlock JL, Downs MF. Social contagion of mental 
health: Evidence from college roommates. Health Econ. 2013;22(8):965-986. PMCID: 
PMC4381550 
160. Crawford SD, Couper MP, Lamias MJ. Web surveys: Perception of burden. Soc Sci 
Comput Rev. 2001;19(2):146-162. 
161. American College Health Association-National College Health Assessment. 
Undergraduate Student Reference Group. 2016. http://www.acha-
ncha.org/docs/NCHAII%20SPRING%202016%20UNDERGRADUATE%20REFERENC
E%20GROUP%20DATA%20REPORT.pdf. 
162. Eisenberg D, Golberstein E, Gollust SE. Help-seeking and access to mental health 
care in a university student population. Med Care. 2007;45(7):594-601. PMID: 17571007 
163. Lipson SK, Gaddis SM, Heinze J, Beck K, Eisenberg D. Variations in student 
mental health and treatment utilization across US colleges and universities. J Am Coll 
Health. 2015;63(6):388-396. PMID: 25942473 
164. Furner CP, Racherla P, Babb JS. Mobile app stickiness (MASS) and mobile 
interactivity: A conceptual model. Market Rev. 2014;14(2):163-188. 
165. Connolly Gibbons MB, Rothbard A, Farris KD, et al. Changes in psychotherapy 
utilization among consumers of services for major depressive disorder in the community 
mental health system. Adm Policy Ment Health. 2011;38(6):495-503. PMCID: 
PMC3350098 
166. Olfson M, Marcus SC. National trends in outpatient psychotherapy. Am J 
Psychiatry. 2010;167(12):1456-1463.PMID: 20686187 
167. Fridberg DJ, Cao D, King AC. Integrating alcohol response feedback in a brief 
intervention for youngadult heavy drinkers who smoke: A pilot study. Drug Alcohol 
Depend. 2015;155:293-297. PMCID: PMC4582006 
168. Norman P, Cameron D, Epton T, et al. A randomized controlled trial of a brief online 
intervention to reduce alcohol consumption in new university students: Combining self-
affirmation, theory of planned behaviour messages, and implementation intentions. Br J 
Health Psychol. 2017. PMID: 28941040 
169. Pedersen ER, Neighbors C, Atkins DC, Lee CM, Larimer ME. Brief online 
interventions targeting risk and protective factors for increased and problematic alcohol 
use among American college students studying abroad. Psychol Addict Behav. 
2017;31(2):220-230. PMCID: PMC5344728 
170. Sainfort F, Becker M, Diamond R. Judgments of quality of life of individuals with 
severe mental disorders: Patient self-report versus provider perspectives. Am J 
Psychiatry. 1996;153(4):497-502. PMID: 8599397 
171. Newman MG, Llera SJ, Erickson TM, Przeworski A, Castonguay LG. Worry and 
generalized anxiety disorder: A review and theoretical synthesis of evidence on nature, 
etiology, mechanisms, and treatment. Annu Rev Clin Psychol. 2013;9:275-297. PMCID: 
PMC4964851 
172. Buntrock C, Ebert DD, Lehr D, et al. Effect of a web-based guided self-help 
intervention for prevention of major depression in adults with subthreshold depression: A 
randomized clinical trial. JAMA. 2016;315(17):1854-1863. PMID: 27139058 
173. Titov N, Dear BF, Staples LG, et al. Disorder-specific versus transdiagnostic and 
clinician-guided versus self-guided treatment for major depressive disorder and 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
38
comorbid anxiety disorders: A randomized controlled trial. J Anxiety Disord. 2015;35:88-
102. PMID: 26422822 
174. Lynch FL, Striegel-Moore RH, Dickerson JF, et al. Cost-effectiveness of guided 
self-help treatment for recurrent binge eating. J Consult Clin Psychol. 2010;78(3):322-
333. PMCID: PMC2880825 
175. Striegel-Moore RH, Wilson GT, DeBar L, et al. Cognitive behavioral guided self-
help for the treatment of recurrent binge eating. J Consult Clin Psychol. 2010;78(3):312-
321. PMCID: PMC2880824 
176. Oulasvirta A, Tamminen S, Roto V, Kuorelahti J. Interaction in 4-second bursts: 
The fragmented nature of attentional resources in mobile HCI. Paper presented at: 
Proceedings of the SIGCHI conference on Human factors in computing systems; 2005. 
177. Vaish R, Wyngarden K, Chen J, Cheung B, Bernstein MS. Twitch crowdsourcing: 
crowd contributions in short bursts of time. Paper presented at: Proceedings of the 32nd 
annual ACM conference on Human factors in computing systems; 2014. 
178. Nitsch M, Dimopoulos NC, Flaschberger E, et al. A guided online and mobile self-
help program for individuals with eating disorders: An iterative engagement and usability 
study. J Med Internet Res. 2016;18(1):e7. PMCID: PMC4726867 
179. Donkin L, Glozier N. Motivators and motivations to persist with online psychological 
interventions: A qualitative study of treatment completers. J Med Internet Res. 
2012;14(3):e91. PMCID: PMC3414905  
180. American College Health Association. American College Health Association-
National College Health Assessment II: Reference Group Executive Summary Spring 
2015; 2015. 
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&ved=0ahUKEwi
SeGd1e_RAhWE64MKHaOODFMQFggaMAA&url=http%3A%2F%2Fwww.achancha. 
org%2Fdocs%2FNCHAII_WEB_SPRING_2015_REFERENCE_GROUP_EXECUTIVE_
SUMMARY.pdf&usg=AFQjCNFOBvLJhYp2jlrSHTOxgUbv4tBteQ&sig2=yezVohCSTTBI
uRb26TOivA&bvm=bv.146073913,d.amc&cad=rjt 
181. Dazzi T, Gribble R, Wessely S, Fear NT. Does asking about suicide and related 
behaviours induce suicidal ideation? What is the evidence? Psychol Med. 
2014;44(16):3361-3363. PMID: 24998511 
182. Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy 
questionnaire. J Behav Ther Exp Psychiatry. 2000;31(2):73-86. PMID: 11132119 
183. Özdel K, Taymur I, Guriz SO, Tulaci RG, Kuru E, Turkcapar MH. Measuring 
cognitive errors using the Cognitive Distortions Scale (CDS): Psychometric properties in 
clinical and non-clinical samples. PLoS One. 2014;9(8):e105956. PMCID: PMC4149474 
184. Newman MG, Zuellig AR, Kachin KE, et al. Preliminary reliability and validity of the 
Generalized Anxiety Disorder Questionnaire-IV: A revised self-report diagnostic measure 
of generalized anxiety disorder. Behav Ther. 2002;33(2):215-233. 
185. Newman MG, Kachin KE, Zuellig AR, Constantino MJ, Cashman-McGrath L. The 
Social Phobia Diagnostic Questionnaire: Preliminary validation of a new self-report 
diagnostic measure of social phobia. Psychol Med. 2003;33(4):623-635. PMID: 
12785464 
186. Newman MG, Holmes M, Zuellig AR, Kachin KE, Behar E. The reliability and 
validity of the Panic Disorder Self-Report: A new diagnostic screening measure of panic 
disorder. Psychol Assess. 2006;18(1):49-61. PMID: 16594812 
187. Moore MT, Anderson NL, Barnes JM, Haigh EA, Fresco DM. Using the GAD-Q-IV 
to identify generalized anxiety disorder in psychiatric treatment seeking and primary care 
medical samples. J Anxiety Disord. 2014;28(1):25-30. PMID: 24334213 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
39
188. Roemer L, Borkovec M, Posa S, Borkovec TD. A self-report diagnostic measure of 
generalized anxiety disorder. J Behav Ther Exp Psychiatry. 1995;26(4):345-350. PMID: 
8675722 
189. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: Validity of a brief depression 
severity measure. J Gen Intern Med. 2001;16(9):606-613. PMID: 11556941 
190. Kroenke K, Spitzer RL. The PHQ-9: A new depression diagnostic and severity 
measure. Psychiatr Ann. 2002;32(9):509-515. 
191. Kroenke K, Spitzer RL, Williams JBW, Löwe B. The Patient Health Questionnaire 
somatic, anxiety, and depressive symptom scales: A systematic review. Gen Hosp 
Psychiatry. 2010;32(4):345-359. PMID: 20633738 
192. Mond JM, Hay PJ, Rodgers B, Owen C, Beumont PJV. Validity of the Eating 
Disorder Examination Questionnaire (EDE-Q) in screening for eating disorders in 
community samples. Behav Res Ther. 2004;42(5):551-567. PMID: 15033501 
193. Berg KC, Peterson CB, Frazier P, Crow SJ. Psychometric evaluation of the eating 
disorder examination and eating disorder examination-questionnaire: A systematic 
review of the literature. Int J Eat Disord. 2012;45(3):428-438. PMCID: PMC3668855 
194. Prins A, Ouimette P, Kimerling R, et al. The primary care PTSD screen (PC-PTSD): 
Development and operating characteristics. Primary Care Psychia. 2003;9(1):9-14. 
195. Morin CM, Belleville G, Bélanger L, Ivers H. The Insomnia Severity Index: 
Psychometric indicators to detect insomnia cases and evaluate treatment response. 
Sleep. 2011;34(5):601-608. PMCID: PMC3079939 
196. Bradley KA, Bush KR, Epler AJ, et al. Two brief alcohol-screening tests from the 
alcohol use disorders identification test (audit): Validation in a female veterans affairs 
patient population. Arch Intern Med. 2003;163(7):821-829. PMID: 12695273 
197. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol 
consumption questions (AUDIT-C): An effective brief screening test for problem drinking. 
Arch Intern Med. 1998;158(16):1789- 1795. PMID: 9738608 
198. Ware J Jr., Kosinski M, Keller SD. A 12-Item short-form health survey: Construction 
of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220-233. 
PMID: 8628042 
199. Gandek B, Ware JE, Aaronson NK, et al. Cross-validation of item selection and 
scoring for the SF-12 Health Survey in nine countries: Results from the IQOLA Project. J 
Clin Epidemiol. 1998;51(11):1171- 1178. PMID: 9817135 
200. Wells KB, Sturm R, Burnam A. National Survey of Alcohol, Drug, and Mental Health 
Problems [Healthcare for Communities], 2000-2001. Ann Arbor, MI: Inter-university 
Consortium for Political and Social Research; 2012. 
201. Brown GK, Karlin BE, Trockel M, Gordienko M, Yesavage J, Taylor CB. 
Effectiveness of cognitive behavioral therapy for veterans with depression and suicidal 
ideation. Arch Suicide Res. 2016;20(4):677-682. PMID: 26983897 
202. Jarrett RB, Vittengl JR, Clark LA, Thase ME. Skills of Cognitive Therapy (SoCT): A 
new measure of patients' comprehension and use. Psychol Assess. 2011;23(3):578-586. 
PMCID: PMC3144321 
203. Brown GK, Thase ME, Vittengl JR, Borman PD, Clark LA, Jarrett RB. Assessing 
cognitive therapy skills comprehension, acquisition, and use by means of an 
independent observer version of the Skills of Cognitive Therapy (SoCT-IO). Psychol 
Assess. 2016;28(2):205-213. PMCID: PMC4564865 
204. Campbell-Sills L, Norman SB, Craske MG, et al. Validation of a brief measure of 
anxiety-related severity and impairment: The Overall Anxiety Severity and Impairment 
Scale (OASIS). J Affect Disord. 2009;112(1-3):92-101. 

 
 
 
 
 
 
______________________________________________________________________________________ 
 
40
205. Norman SB, Hami Cissell S, Means-Christensen AJ, Stein MB. Development and 
validation of an overall anxiety severity and impairment scale (OASIS). Depress anxiety. 
2006;23(4):245-249. PMID: 16688739 
206. Bragdon LB, Diefenbach GJ, Hannan S, Tolin DF. Psychometric properties of the 
Overall Anxiety Severity and Impairment Scale (OASIS) among psychiatric outpatients. J 
Affect Disord. 2016;201:112- 115. PMID: 27195516 
207. Norman SB, Allard CB, Trim RS, et al. Psychometrics of the Overall Anxiety 
Severity and Impairment Scale (OASIS) in a sample of women with and without trauma 
histories. Arch Womens Ment Health. 2013;16(2):123-129. PMID: 23296334 
208. Berg KC, Peterson CB, Frazier P, Crow SJ. Convergence of scores on the 
interview and questionnaire versions of the Eating Disorder Examination: A meta-
analytic review. Psychol Assess. 2011;23(2):714- 724. PMCID: PMC3437603 
209. Killen JD, Taylor CB, Hayward C, et al. Weight concerns influence the development 
of eating disorders: A 4-year prospective study. J Consult Clin Psychol. 1996;64(5):936-
940. PMID: 8916622 
210. Newman MG, Fisher AJ. Expectancy/credibility change as a mediator of cognitive 
behavioral therapy for generalized anxiety disorder: Mechanism of action or proxy for 
symptom change? Int J Cogn Ther. 2010;3:245-261. PMCID: PMC2995495 
211. Reinecke A, Waldenmaier L, Cooper MJ, Harmer CJ. Changes in automatic threat 
processing precede and predict clinical changes with exposure-based cognitive-behavior 
therapy for panic disorder. Biol Psychiatry. 2013;73(11):1064-1070. PMID: 23510582 
212. Tang TZ, DeRubeis RJ, Beberman R, Pham T. Cognitive changes, critical 
sessions, and sudden gains in cognitive-behavioral therapy for depression. J Consult 
Clin Psychol. 2005;73(1):168-172. PMID: 15709844 
213. Scott SD, Plotnikoff RC, Karunamuni N, Bize R, Rodgers W. Factors influencing the 
adoption of an innovation: An examination of the uptake of the Canadian Heart Health 
Kit (HHK). Implement Sci. 2008;3(1):41. PMCID: PMC2567341 
214. Abraham WT, Russell DW. Statistical power analysis in psychological research. 
Soc Personal Psychol Compass. 2008;2(1):283-301. 
215. Arnold BF, Hogan DR, Colford JM, Jr., Hubbard AE. Simulation methods to 
estimate design power: An overview for applied research. BMC Med Res Methodol. 
2011;11:94. PMCID: PMC3146952  
216. Kraemer HC. Messages for clinicians: Moderators and mediators of treatment 
outcome in randomized clinical trials. Am J Psychiatry. 2016;173(7):672-679. PMID: 
26988629 
217. Drummond M, Sculpher M, Torrance G, O'Brien B, Stoddart G. Methods for the 
Economic Evaluation of Health Care Programmes. 3rd ed. Oxford, England: Oxford 
University Press; 2005. 
218. Kymes SM. An introduction to decision analysis in the economic evaluation of the 
prevention and treatment of vision-related diseases. Ophthalmic Epidemiol. 
2008;15(2):76-83. PMID: 18432490 
219. O'Hagan A, McCabe C, Akehurst R, et al. Incorporation of uncertainty in health 
economic modelling studies. Pharmacoeconomics. 2005;23(6):529-536. PMID: 
15960550 
220. Krahn MD, Naglie G, Naimark D, Redelmeier DA, Detsky AS. Primer on medical 
decision analysis: Part 4—Analyzing the model and interpreting the results. Med Decis 
Making. 1997;17(2):142-151. PMID: 9107609