Study Title: Goal Elicitation, Treatment Prioritization, & Electronically-Practiced Discussion - Pilot Study
Phase: NA
Conditions: Mental Illness, Shared Decision Making
Sponsor: Indiana University

Cover Page 
 
Protocol (Including Statistical Analysis Plan) 
 
Study Title: Goal Elicitation, Treatment Prioritization, & Electronically-Practiced Discussion – 
Pilot Study (Aims 2 and 3) 
 
NCT04601194 
 
Document Date: 5/3/2022 

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Study Title: Goal Elicitation, Treatment Prioritization, & Electronically-Practiced Discussion – 
Pilot Study (Aims 2 and 3) 
 
 
Principal Investigator: Michelle Salyers, PhD 
Co-principal Investigator: Adam Hirsh, PhD 
 
 
1.0 Background and Significance  
 
The importance of Shared Decision-Making (SDM) in Mental Health 
Community mental health providers frequently struggle to integrate patient-centered,† recovery-oriented care 
with effective psychiatric medication management for people with severe mental illness (SMI). Indeed, despite 
long-standing efforts to implement patient-centered care, people with SMI are frequently not active participants 
in treatment decision-making, particularly with regard to psychiatric treatment decisions1,2 – this is often due to 
a mismatch between treatment recommendations and “personal medicines” (individual strategies that people 
find important for their own recovery).3 Effective illness management involves complex decision-making and 
requires a partnership between two experts, the patient and the provider.4 When SDM takes place, patients 
and providers share information, express preferences, discuss uncertainties (e.g., pros/cons of options, risks), 
and ultimately agree on a plan.5 SDM is vital to patient-centered care – fostering autonomy and efficacy in 
managing health – because it is ultimately the patients who live with the consequences of treatment.5-7 
Moreover, SDM has been associated with positive outcomes in adults with SMI, including higher quality 
patient-provider interactions, better coping, and higher treatment adherence.4,8  
 
This concept of SDM is now widely recognized as an indicator of high-quality healthcare,9 with increasing 
emphasis in mental health settings.4,6,10,11 Most patients, including those with SMI, desire a role in treatment 
decisions,12 and SDM is especially relevant for people who have persistent symptoms, for which preferred 
treatments vary widely. Unfortunately, SDM is still relatively uncommon in community mental health,13 and few 
studies have evaluated approaches specifically designed to increase SDM in these settings. In addition, 
several barriers have been identified that impede widespread use of SDM for adults with SMI, including 
provider concerns of time constraints or applicability for some patients, and confusion around roles and 
responsibilities.14,15 Patients might not be prepared to interact actively with providers or be aware of multiple 
options for managing illness. Given these barriers to SDM, approaches are needed to promote active patient 
involvement and reciprocal exchange of information and preferences that facilitate recovery outcomes.  
 
 
† We recognize that the mental health field uses a wide variety of terms to refer to people who are using treatment 
services (e.g., patient, consumer, client, service-user, survivor). Here we use the term “patient” to be consistent with the 
broader medical field and with the concepts of patient activation and patient-centered care. We use the term “provider” to 
describe professional health care providers. 

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Conceptual Framework for Activating Patients for Shared Decision-Making   
The Chronic Care Model (CCM) and Self-Regulation Theory provide the theoretical foundation for our 
intervention: GET PrEPD-Psychiatry. The CCM (Fig 1) emphasizes productive interactions between patients 
and providers within the larger context of the health system and community.16,17 Of particular relevance is the 
model’s notion of collaborative management, which takes 
place when patients and providers have a productive 
working relationship, well-articulated goals, and shared 
understanding of their roles.18 Under a model of 
collaborative care, patients and providers work together, 
through sharing information and decisions, to find optimal 
treatments.17 This requires an “activated patient,” who 
takes initiative to ask questions and give information to 
providers that reflect the patient’s priorities in seeking 
care, and who is an equal partner with his/her 
provider.17,19,20 The CCM’s emphasis on patients’ roles in 
collaborative management is supported by studies 
showing that patient activation is critical for effective 
participation in care, including decision-making.21-23 Self-
Regulation Theory24,25 informs the central focus of GET 
PrEPD-Psychiatry on goals, because clarifying, prioritizing, 
and articulating goals are critical for treatment decision-
making. According to Self-Regulation Theory, people 
organize their lives around the pursuit of personally meaningful goals, which are arranged hierarchically, such 
that specific goals (e.g., reduce voices, improve mood) serve to achieve broader goals (e.g., be a better 
parent, go back to college). However, goals can conflict with one another, and patients may confuse levels of 
abstraction, focusing on higher-order goals without first addressing more specific goals.26,27 GET PrEPD-
Psychiatry will help patients organize, understand, prioritize, and ultimately articulate these goals to providers. 
 
Interventions to Improve Shared Decision-Making in Mental Health 
Decision support systems. Few interventions have been designed specifically to address SDM in people with 
SMI. Perhaps the most comprehensive is CommonGround, which incorporates a variety of strategies, including 
education and self-management materials, computer technology, decision-aids, peer support and coaching in 
how to make decisions, and training for service users as well as providers (both prescribing and supporting, 
e.g., peer providers, case managers).28,29 Computer kiosks located in a designated decision support center 
(e.g., room ideally near the meetings with providers) use self-guided discovery modules designed to help 
individuals learn about recovery and identify strategies to reach recovery goals. Goals take the form of “power 
statements,” succinctly worded to contextualize medication management preferences in line with personal 
recovery goals. In addition, CommonGround emphasizes “personal medicines,”3 which are self-identified 
strategies that provide meaning and help patients stay well, in addition to or instead of prescribed medication. 
Prior to a psychiatric visit, patients complete a one-page health report that integrates power statements and 
personal medicines with current symptoms and concerns to share with the prescriber. Trained peers facilitate 
use of the CommonGround software, and additional recovery tools (e.g., videos, worksheets) are available for 
patients and providers. Entire treatment teams are trained using the system. 
 
CommonGround has received awards for innovation,30 and initial pilot work suggested improved 
communication and a greater emphasis on recovery-oriented goals.28 Three subsequent evaluations reported 
varied findings, depending on the outcomes assessed. MacDonald-Wilson and colleagues 31 implemented 
CommonGround across a large healthcare system and found significantly improved symptoms and functioning 
and fewer concerns about medication side effects. CommonGround was not associated with changes in 
medication use.32 Members of our own research team received NIMH funding (R34MH093563; Salyers PI) to 
test CommonGround in four community mental health teams. In an uncontrolled, pre-post evaluation, we found 
improved self-reported symptoms and recovery attitudes, and patients were rated by providers as being more 
involved over time.33 However, we encountered a number of barriers in implementation. Few patients used the 
system consistently. For example, only 22% completed more than three health reports over approximately 18 
Figure 1. 

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months,33 and teams varied greatly in their use of the system, with more frequent use observed among teams 
that had high investment from the prescribing provider and team leader.34 Key informants and fidelity reports 
indicated a number of barriers, including technological difficulties and increased staff burden, as well as 
contextual barriers, including a poor perceived fit with service structure, inconvenient decision support center 
location, low staff investment, and high turnover.34 With taxed resources and strains on providers,35,36 these 
types of system requirements can be difficult for organizations to overcome, requiring a high level of leadership 
commitment, mobilization of resources, and culture change throughout an organization. In fact, although key 
informants in our study praised qualities of CommonGround, the organization discontinued the program due to 
significant financial constraints involved with ongoing training and subscription costs. One alternative to 
CommonGround that also seeks to improve SDM in mental health is to provide more targeted coaching for 
patients. 
 
Coaching interventions. Early studies in the medical field showed that relatively brief interventions to activate 
patients, such as a 20-minute coaching session just prior to a medical visit, could have lasting health 
benefits.37 Brief, pre-visit interventions have also been applied in the mental health field, but with mixed results. 
For example, Hamann and colleagues tested “question prompt sheets” for 100 outpatients with depression.38 
Just prior to a visit with the psychiatrist, half the participants were randomly assigned to complete a worksheet 
describing what they wanted to get out of the session and to identify questions for their provider. Compared to 
treatment as usual, the prompt sheets were not effective in changing patient behavior during a consultation. In 
contrast, Alegría and colleagues40,41 developed a more active coaching intervention for mental health care. The 
DECIDE approach incorporates three individual coaching sessions with cognitive-behavioral strategies (e.g., 
role play, homework) to prepare participants for visits with providers and empower them to take a more active 
role in care. Coaches also discuss cultural issues that might impede taking a more active role. The initial 
evaluation was promising, showing improved self-reported activation and greater retention and attendance in 
subsequent services.40 The intervention was later tested in a more rigorous, multisite RCT with a diverse 
sample.41 Compared to patients who received a mental health brochure, those randomized to DECIDE had 
significantly improved activation and self-management; however, effects were not seen for retention and 
attendance in services for the 6-month period after the intervention. The trial did not report effects on SDM. 
 
Hamann and colleagues42 pilot tested a coaching intervention that directly targeted SDM for inpatients with 
schizophrenia. Their approach involved five, 1-hour group sessions to teach motivational and behavioral 
aspects of being involved in treatment decisions, including role-playing and encouraging peer support. 
Compared to controls, patients in the training group reported greater preference for involvement in and 
responsibility for decisions, after treatment and 6 months later, but no changes in decision self-efficacy. In 
addition, those in the training group reported less trust in providers, and providers rated relationships with 
these patients as more difficult. Thus, there may be unintended consequences to the patient-provider 
relationship with coaching alone. In addition, this intervention did not explicitly report patient goal setting and 
prioritization as a key element, which are critical pre-cursors to effective SDM. 
 
GET PrEPD (Goal Elicitation, Treatment Prioritization, & Electronically-Practiced Discussion) 
GET PrEPD incorporates several strengths of the above SDM interventions and is designed to increase patient 
activation and communication self-efficacy (See Fig 2). As detailed below, this is accomplished by working with 
patients to (1) elicit and discuss their goals, priorities, and preferences; (2) develop skills, particularly agenda 
setting and question asking, to communicate these goals, priorities, and preferences with providers; and (3) 
practice these skills in an interactive format with Virtual Providers. The intervention was initially developed for 
use in chronic pain patients (see Section C1 below). Given similar needs for helping patients with SMI be more 
active and develop skills to effectively participate in SDM, we believe GET PrEPD can also be applied to this 
population. However, we needed to modify the content of the coaching sessions to be specific to people with 
SMI and program the Virtual Provider to be specific to areas of concern expressed in psychiatric visits. Based 

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on the Chronic Care Model 
(CCM) and Self-
Regulation Theory 
described above, we have 
identified specific targets 
through which the modified 
intervention (“GET PrEPD-
Psychiatry”) can lead to 
better outcomes. In the 
following sub-sections 
(and Table 1), we describe 
the two targets and how 
GET PrEPD-Psychiatry 
addresses them. We also 
detail the unique features 
of GET PrEPD-Psychiatry, 
contrast it with other SDM approaches, and describe how these unique features increase GET PrEPD-
Psychiatry’s likely effectiveness on a broad scale. 
 
Target I: Patient Activation. Patient activation refers to having the knowledge, skills, and confidence to 
manage one’s health, and is an important precursor to SDM.43-45 GET PrEPD-Psychiatry will increase patient 
activation by (1) eliciting and helping patients to  
prioritize their treatment goals, (2) eliciting patients’ 
treatment preferences and rationale for those 
preferences, (3) connecting treatment goals to 
patients’ life goals and values, and (4) connecting 
treatment goals to other important people in patients’ 
lives. The coach begins by eliciting and discussing 
patients’ treatment goals, priorities, and preferences. 
This exploration is akin to identifying personal 
medicines in CommonGround and allows patients to 
actively consider which goals are most important to 
them, which treatments they prefer in pursuing those 
goals, and their reasons for these priorities and 
preferences. This self-awareness is a critical first step 
toward patient activation, as this helps to increase 
patient knowledge of their condition, which is essential 
for SDM.21,23,46 During the next session, the coach helps patients elaborate and clarify treatment goals, 
priorities, and preferences by asking how psychiatric treatment fits into patients’ broader life contexts, 
particularly personal values and connections to family and friends. That is, GET PrEPD-Psychiatry will help 
patients clarify why their goals and preferences matter, thereby fostering motivation and engagement in 
actively managing their condition. Importantly, through goal clarification, GET PrEPD-Psychiatry is 
sensitive to individual differences, which is critical for people with SMI who may have similar needs as 
a population, yet are individuals who should not be treated as a homogenous group. This helps ensure 
personalized treatment.  
 
GET PrEPD-Psychiatry will help patients formulate and practice asking questions. This will increase patient 
activation by ensuring the unique information needs of patients are met during the visit. Indeed, information 
seeking is a fundamental skill in effective patient-provider communication.47 As described above, similar goal 
setting and coaching takes place in Alegría’s DECIDE intervention, which was shown to increase patient 
activation in people with mental illness.41 GET PrEPD-Psychiatry will re-assess patient goals, priorities, and 
preferences across sessions in recognition of the dynamic nature of patients’ lived experiences and mental 
health treatment. This will enhance patient activation by giving them the opportunity to re-evaluate how a 
decided-upon treatment approach is working.  
Table 1. Targets of Goal Elicitation, Treatment 
Prioritization, & Electronically-Practiced Discussion (GET 
PrEPD-Psychiatry) Intervention 
Target 1: Patient Activation 
Target 2: Communication 
Self-Efficacy 
Elicit Patient Treatment Goals 
Agenda Setting 
Prioritize Treatment 
Goals 
Stating Treatment Goals 
& Preferences 
Elicit Patient Treatment 
Preferences 
Handling Interruptions 
Discuss Rationale for 
Preferences 
Asking Specific Questions 
Connect Treatment Goals to 
Patient Life Goals & Values 
Communicating Goal Context 
to Provider 
Connect Treatment Goals to 
Important People in Patient’s 
Life 
Discussing Pros & Cons of 
Treatment Options with 
Provider 

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Target II: Communication Self-Efficacy. Communication self-efficacy refers to patients’ confidence 
interacting with providers and is critical for participation in SDM, as patients must be able to communicate 
goals and preferences to providers to reach a mutually agreeable decision.5,48 GET PrEPD-Psychiatry will 
increase communication self-efficacy by allowing patients to practice newly learned skills with a Virtual 
Provider in a non-threatening, individualized format, thereby facilitating increased confidence as practice 
progresses. During each session, the coach introduces patients to specific skills necessary for effective 
communication with providers (e.g., agenda setting). The coach then models and role-plays the skills with 
patients. Finally, patients are encouraged to practice the skills with the Virtual Provider program, allowing for 
multiple, tailored practice sessions. The practice with the Virtual Provider program is a key element of GET 
PrEPD-Psychiatry and unique from other interventions. 
 
GET PrEPD-Psychiatry focuses on 4 communication skills: (1) agenda setting, (2) asking questions, (3) 
communicating goal context, and (4) discussing pros/cons of treatment options. Agenda setting skills help 
patients ensure their central goals, priorities, and preferences are discussed in the visit.49,50 Helping patients to 
formulate effective questions and engage providers in instructive dialogue about their concerns enables them 
to obtain tailored information and creates an atmosphere conducive to SDM.51 For example, by developing 
skills to communicate how treatment goals fit into their broader life context, patients are better equipped to 
elicit understanding and empathy from providers. Developing discussion skills (e.g., pros/cons) prepares 
patients to “hold their own” when negotiating treatment with providers and, ultimately, to make an informed, 
collaborative decision.52   
 
Skills Practice. Although interventions to enhance patient activation and communication exist in mental 
health,41,42 GET PrEPD-Psychiatry is innovative by allowing individualized, repeated practice of skills using the 
Virtual Human system. Patients practice these skills by interacting with 3D animated Virtual Providers who 
display gestural and facial expressions that are triggered by patients’ 
questions/statements (see Fig 3 for sample). To personalize the practice 
sessions, patients can select the gender and race of the Virtual Provider. 
Patients interact with Virtual Providers by selecting questions/statements from 
a menu (with the option of using a typed interface only in the “Questions about 
my treatment” category). Providing pre-populated choices is easier for users, 
especially patients with lower health literacy and who are less technologically-
savvy, particularly during early stages of skill acquisition. Provider responses 
are transmitted with on-screen text, pre-recorded audio, and animations (non-
verbal behavior). The system maintains a log of practice frequency/duration, which topics were selected and in 
what order for each patient (using assigned Research IDs), which allows for monitoring of progress (and 
subsequent troubleshooting during individual coaching sessions) and examining the relationship between 
amount of skills practice and our outcome measures. The system will also maintain a log of which Virtual 
Provider was chosen, if the Welcome Video was viewed, and the number of times the Help and Go Back to 
Main Page buttons were selected. 
 
Impact on Science, Practice, and the Field 
This R34 is the next logical step in a program of research to help activate people with SMI to be more 
involved in treatment decisions as a critical part of self-management and recovery. The GET PrEPD-Psychiatry 
intervention builds on strengths of prior promising SDM interventions, while aiming to overcome key barriers of 
those approaches. The proposed project will result in a technologically advanced, ecologically-valid, and 
highly scalable approach to activating patients and enhancing their communication skills for participation in 
SDM. This project will also increase scientific knowledge by establishing feasibility and acceptability of 
this approach in community mental health settings. Importantly, we will also be examining proximal targets for 
the intervention – patient activation and communication self-efficacy – that can lead to better SDM, and 
ultimately to better recovery outcomes. These are critical steps before GET PrEPD-Psychiatry can be 
rigorously tested in a large, multi-site clinical trial. Ultimately, successful completion of our work will yield an 
innovative and scalable approach to addressing a critical issue for mental health services. 
 
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We have created a Virtual Provider program (described above in Skills Practice) to be specific to psychiatric 
decision-making. The interactive practice sessions will consist of real-time, dynamic communication with a 
Virtual Provider, with a particular emphasis on practicing skills in agenda-setting and question-asking, and 
articulating goals, priorities, and treatment preferences. This innovative program allows us to create practice 
scenarios consistent with effective learning principles, thus maximizing participants’ mastery of skills. Prior 
patient-focused SDM interventions included minimal skills practice – typically simple role-playing with a coach 
during training sessions.41,42 By contrast, learning research and theory indicate that to master new skills, 
practice must be (a) frequent and (b) tailored to the individual’s learning needs/preferences, and (c) the 
practice environment must approximate the context in which the skills will be employed.67-70,73 As detailed 
above, our intervention allows patients to access the online platform to practice as often as they like, separate 
from coaching sessions; and, will use high-fidelity computer simulations based on conversations from real 
psychiatry visits to better approximate an actual clinical context. Moreover, the intervention is efficient, 
targeting patients with 4 coaching sessions and online, self-directed practice. Our system is innovative, 
consistent with best learning practices, and allows for widespread dissemination if GET PrEPD-Psychiatry is 
effective. 
 
Preliminary Studies 
Our team has an extensive history of grant-funded, published research that directly informs this proposal. The 
investigators bring complementary expertise: patient activation and SDM for people with SMI (Salyers) and 
Virtual Human Technology and developing GET PrEPD for people with chronic pain (Hirsh).  Other 
investigators bring additional expertise in communication self-efficacy, SDM, Virtual Providers, and goal-
setting. 
 
Patient Activation, Communication Self-Efficacy, and Shared Decision-Making. Dr. Salyers has a long 
history of research focused on helping people with SMI better manage illness and achieve personal recovery 
goals. Her early work focused on Illness Management and Recovery (IMR), a curriculum based program 
incorporating evidence-based practices to help patients learn self-management skills.74 She has completed 
several studies of IMR, including a VA-funded RCT75 and federally-funded implementation studies.74,76-78 The 
VA-funded RCT included a sister study, also funded by VA (Mechanisms of Patient Activation and Self-
Management in Schizophrenia, VA HSR&D: IIR 08-324-1), where she partnered with Dr. Matthias to better 
understand the concept and correlates of patient activation in people with SMI through narratives of recovery 
from illness.79,80 Drs. Salyers and Matthias also collaborated in observing how patients might be active in 
discussions with their treatment providers.81 Through her work in self-management training, Dr. Salyers 
recognized that although the IMR program helped patients identify recovery goals and learn important self-
management skills, IMR did not directly address how to work with treatment providers more effectively. This 
led to her work in SDM, first to better understand how to measure it, and then implementing programs to 
improve SDM for people with SMI. Drs. Salyers, Matthias, and Fukui adapted a coding system to measure 
observed SDM in psychiatric visits,23,45,80,82,83 which we will be using in the proposed study to determine the 
impact of GET PrEPD-Psychiatry on actual decision-making in sessions with providers. As described above, 
Dr. Salyers recently led a team, including Dr. Fukui, to implement and evaluate CommonGround.33,34 We 
successfully recruited and followed 167 patients (and their providers) at Eskenazi (formerly) Midtown 
Community Mental Health Center (now, Sandra Eskenazi Mental Health Center), our partner agency in the 
proposed GET PrEPD-Psychiatry study. In addition, we audio recorded visits between participants and their 
prescribing providers at baseline and at 12- and 18-month follow-ups. We used the transcripts from these 
observations to adapt the Virtual Providers in Aim 1, and attempted to use the same procedures of audio 
recording sessions to gather data for Aim 3. Ultimately, the challenges we experienced in implementing 
CommonGround led us to conclude that the unique aspects of GET PrEPD-Psychiatry are necessary for 
effective change in patients’ participation in SDM. That is, we aim to build on some of the strengths of 
CommonGround (i.e., goal setting and prioritization, coaching), while finding an effective and less resource-
intensive way to engage patients more actively with treatment providers. GET PrEPD-Psychiatry holds the 
promise of fulfilling these aims. 
 
Virtual Human Technology.  A key innovation of GET PrEPD-Psychiatry – that will maximize its effectiveness 
and scalability – is the practice of skills with a Virtual Provider program. Dr. Hirsh at IUPUI and Dr. Lok at the 

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University of Florida have been leaders in research on Virtual Human technology and pain. They have 
leveraged this methodology for its enhanced experimental control (allowing rigorous hypothesis testing) and 
realism (approximating real-world situations) to improve pain care for vulnerable populations, including low-
income and Black patients. Their grant-funded work has soundly established the reliability, validity, and 
feasibility of using Virtual Human technology to study decision-making, patient-provider communication, and 
disparities in pain care.53-63,65,66,84-86 Of particular relevance to GET PrEPD-Psychiatry, Dr. Hirsh’s current 
NIMHD-funded R01 uses the Virtual Human platform to facilitate real-time, dynamic communication between 
physician subjects and virtual patients with pain. This physician-virtual patient interaction is part of an RCT 
testing an intervention to enhance physician perspective-taking and reduce treatment disparities for low-
income minority patients. Over 80 physicians have completed the intervention arm of the trial (>85% 
completion rate) using the Virtual Human interaction system, and the results provide strong evidence of the 
system’s success in terms of conversational accuracy and subject engagement. Our team has also created 
Virtual Providers for communication training. In an NSF-funded project, we used Virtual Providers to train 
operating room staff on best communication practices for drug administration and patient safety.87 Our other 
studies have found that people resolve conflicts similarly with Virtual Providers as with human providers88 and 
that Virtual Providers can influence participants to use evidence-based teamwork protocols for patient 
safety.89,90 Moreover, participants described the Virtual Provider interactions as highly realistic and stated that 
the learning opportunities were valuable. The empirical work highlighted above is supported by earlier studies 
demonstrating high correlations between Virtual Human communication and communication with real 
humans.91,92  
 
Goal Setting and Prioritization. Complementing our expertise in activation, communication, and SDM, our 
team has conducted research related to patients’ symptom priorities and treatment goals for people with a 
variety of chronic conditions. As noted above, Dr. Salyers’ work with IMR incorporates goal setting as an 
important part of self-management. Drs. Hirsh, Matthias, and Rand collaborated on grant-funded (Walther 
Cancer Foundation, Indiana University), mixed-methods studies examining symptom and treatment priorities of 
cancer patients. They observed large individual differences in patient perspectives on the symptoms that are 
most important to prioritize, the criteria by which patients judge treatment effectiveness, and how symptom and 
treatment priorities are related to their broader values and goals93,94 – these results underscore the need for 
patient-centered interventions tailored to individual goals and priorities. This work complements a recent pilot 
study conducted by Drs. Hirsh and Rand (funded by Indiana University) using a similar goal elicitation and 
clarification method in chronic pain patients receiving care at Eskenazi Health, as well as earlier studies by Dr. 
Hirsh on chronic pain patients’ symptom and treatment priorities, treatment engagement, and satisfaction with 
care.95-98 Based on this and other grant-funded (American Cancer Society) work by Dr. Rand,99-101 our team 
has developed a reliable method for eliciting patients’ treatment goals, clarifying these goals in the broader 
context of patients’ lives, and determining the subjective importance of the treatment goals to patients. This 
method serves as the basis for GET PrEPD-Psychiatry. 
 
2.0 Research Design Overview 
 
GET PrEPD-Psychiatry is a mixed-methods, developmental study to adapt an SDM intervention to be specific 
for psychiatry decisions (Aim 1, IRB #1807699331), evaluate its feasibility and acceptability (Aim 2), and 
examine potential mechanisms of change and preliminary outcomes (Aim 3) of this innovative intervention to 
increase SDM and self-management for adults with SMI. In line with NIMH priorities, we are examining 
whether GET PrEPD-Psychiatry engages the target mechanisms that putatively underlie the intervention (i.e., 
patient activation and communication self-efficacy; Aim 3). For Aim 1, we used approximately 200 transcripts 
from our prior study of SDM in psychiatry to cull language used in decision-making. These conversations were 
used to program the Virtual Provider to represent common interactions and decisions in psychiatric visits. We 
iteratively tested the use of the Virtual Provider program with patients and obtained feedback from our 
psychiatry consultants to refine the program. For Aim 2, we will recruit up to 40 patients to participate in GET 
PrEPD-Psychiatry (4 approximately weekly goal setting/coaching sessions, coupled with Virtual Provider 
program training and practice). We will assess participant satisfaction and utility ratings, as well as track their 
use (frequency and time-on-task) of the Virtual Provider program. For Aim 3, we will follow enrolled patient 
participants to interview them. We hypothesize that participants will have significantly 1) improved 

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mechanisms of change, demonstrated by increases in self-reported activation and communication 
self-efficacy, 2) improved SDM, and 3) improved self-management and recovery attitudes. 
 
While we hypothesize that this intervention will increase self-reported activation and communication self-
efficacy, improve SDM, and improve self-management and recovery attitudes, there is no evidence to suggest 
that the intervention may cause worsening of these. There is the possibility that there will be no change.  In Dr. 
Matthias’ COOPERATE study in which Drs. Salyers, Hirsh, and Rand are co-investigators (IRB #1712397218), 
a similar coaching intervention is used; 76 subjects have completed the intervention so far. There have been 
no risks identified to suggest the intervention is associated with negative outcomes for subjects. Potential risks 
of the study include feeling uncomfortable, nervous, or anxious, as well as the risk of loss of confidentiality. 
 
3.0 Setting, Recruitment, and Enrollment 
 
We will recruit up to 40 patients at the Sandra Eskenazi Mental Health Center (SEMHC, formerly Midtown 
Mental Health Center) who are being seen in outpatient services for adults with SMI. In our prior study with 
these teams, over half of participating consumers were male (56.9%) and African American (54.8%). Most 
participants were diagnosed with schizophrenia (67.6%); other frequent diagnoses included bipolar disorder 
and major depression. Following procedures established in our CommonGround study 33 and our earlier pilot 
study on patient activation,81 we will recruit patients in the clinic, as they check in for a visit. The receptionist or 
other clinic staff will notify patients of an opportunity to learn more about the study and may distribute brief 
information about the study (recruitment flyer). Recruitment flyers may also be posted or distributed to potential 
subjects. If interested, patients will be referred to the RA or to call the phone number on the recruitment flyer 
with any questions or to find out more information.  RAs will be stationed in the waiting area on specific, pre-
determined days and be available to describe the study, complete informed consent, complete a baseline 
interview, and attempt to audio-record the visit with prescribers who have already given consent to participate. 
There may be times audio-recording the prescriber visit is not feasible (e.g., subject eligibility not complete 
prior to appointment time with prescriber or the prescriber has not provided consent). Following the initial visit, 
we will complete the first session of the intervention or, if needed, arrange a time for this session to be 
completed within approximately 10 days. Because the treatment teams serve people with SMI who may have 
more difficulties in patient activation and SDM skills, we will be able to test feasibility and acceptability with 
people who experience a range of difficulties. The follow-up measures can occur any time after coaching 
session 4, within about 3 months of baseline. Audio-recording of the psychiatry visit with consented prescribers 
will take place approximately 3 months after baseline – based on clinic flow, we expect this will be the next 
available clinic visit after completion of the intervention, but this could vary somewhat for individual patients. 
Participants will be paid $25 for each research assessment visit completed (baseline and 3 months), for a total 
of $50 per participant. 
 
Although GET PrEPD-Psychiatry is a patient-focused intervention, psychiatric providers (e.g., psychiatrists, 
nurse practitioners providing psychiatric care) will also be recruited (from SEMHC) because we will attempt to 
audio-record clinic visits for enrolled patients. To minimize burden, provider involvement is limited to allowing 
up to two psychiatric care visits per patient to be recorded (one at baseline and one again approximately 3 
months later after patients have completed the intervention). While patients will be aware that they are learning 
skills to be more active and effective communicators and more involved in decision-making about their 
psychiatric care, providers will be told more generally that patients are learning communication skills, so as not 
to prime providers to place a greater emphasis on SDM. Potentially eligible providers will be individually invited 
to participate by a research team member by email, phone/video call, or in person interaction and informed 
consent will be obtained. Patients who receive mental health care at the SEMHC may be eligible for the study 
even if their prescriber has not provided consent (e.g., prescriber declined participation, prescriber is at a 
different location). In those cases, no attempt to audio-record prescriber appointments will take place. As of 
January 2022, 4 prescribers consented to participate; however, recording appointments has proven to not be 
particularly feasible and it will not be possible to analyze pre-post changes as initially proposed. No further 
attempts will be made to audio-record prescriber appointments. 
 
4.0 Eligibility Criteria 

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Providers:  
Must be a prescribing provider (e.g., psychiatrist, nurse practitioner providing psychiatric care) at SEMHC who 
provides care to adult outpatients 
 
Patients: 
Inclusion Criteria 
• 
At least 18 years of age 
• 
Current outpatient receiving mental health care at SEMHC 
• 
English speaking 
• 
Willing to participate in 4 approximately weekly coaching sessions, engage in practice with the Virtual 
Provider Program, allow prescriber appointments (if prescriber has provided consent) and coaching 
sessions to be audio recorded* 
 
Exclusion Criteria 
• 
No reasonable access to the internet on a computer 
• 
Inability or unwillingness to use a computer (necessary for Virtual Provider Program practice) 
• 
Never or rarely uses a computer or similar device (based on self-report) 
• 
Very or somewhat uncomfortable using a computer or similar device (based on self-report) 
 
*Research assistants will always ask permission prior to beginning audio recording and subject can refuse individual 
audio recording events and continue participation in the study. 
 
4.0 Outcome Measures, Training, Fidelity 
 
We will measure each component of our model, with the exception of observed SDM, (Figure 2 and Table 2) in 
addition to demographics and chart-reported diagnoses. We chose brief validated measures for low participant 
burden. Aside from Feasibility (Aim 2) and SDM ratings (Aim 3), the remaining measures are self-report. 
Acceptability (Aim 2) is only measured after the intervention, and all others are administered at both baseline 
and 3 months. From each enrolled subject’s medical record, we will collect recent primary and secondary 
mental health diagnoses made by a prescriber (or other mental health provider if prescriber diagnoses are not 
available) in relation to the baseline visit date. 
 
Feasibility Assessment (Aim 2). Feasibility will be assessed by (1) tracking the number of participants who 
sign up for the intervention, and recording the number of people we invited in order to meet sample size goals 
and the reasons for declining; (2) tracking the number of enrolled participants who attend and complete the 
coaching sessions; and (3) tracking use of the online Virtual Provider program (which is accessible from any 
computer) via time-stamp data showing how frequently each participant accessed the online program and how 
much time they spent – during individual sessions and in total – using the program. In terms of benchmarks, 
the percentage of people who agree to enroll will inform the planning for our next study – a full-scale clinical 
trial – by allowing us to estimate the number of participants we need to engage in order to meet sample size 
goals for a fully powered trial.  
 
Table 2. Measures 
Aim 
Construct 
Measure 
Items 
2 
Acceptability* 
Narrative Evaluation of 
Intervention Interview (NEII) 
16 
3 
Patient 
Activation 
Patient Activation Measure for 
Mental Health (PAM-MH) 
13 
3 
Patient 
Activation 
Altarum Consumer 
Engagement (ACE) Measure 
12 
3 
Communicatio
n Self-Efficacy 
Perceived Efficacy in Patient-
Provider Interactions (PEPPI-5) 
5 

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Acceptability Assessment (Aim 2). In this open 
trial, we will assess participants’ satisfaction with 
the training by asking them to complete the 
Narrative Evaluation of Intervention Interview105 
that we have used in other interventions with 
people with SMI.106,107 These questions will focus 
on participant feedback on the whole intervention 
as well as specific components that were most and 
least helpful to them (e.g., goal setting, coaching 
sessions, Virtual Provider). 
 
Target Mechanisms (Aim 3). Patient activation will be measured with the 13-item Patient Activation 
Measure Short Form-Mental Health (PAM-MH),108 which assesses patient knowledge, skill, and confidence for 
self-managing one’s chronic health condition. The PAM-MH has shown good reliability (α = .87-.88) and 
validity in many studies, including our own.81,108-111 
 
Communication self-efficacy will be measured with the Perceived Efficacy in Patient-Physician Interactions 
Scale (PEPPI-5), a 5-item scale that measures patients’ self-efficacy in obtaining medical information and 
getting their most important health concern discussed in a clinic visit. The PEPPI-5 has demonstrated 
responsivity to interventions to activate adults with SMI.41,112 We will also use the Altarum Consumer 
Engagement (ACE) Measure that assesses commitment, informed choice, and navigation.  Utilizing both of 
these measures of patient activation for this pilot test will inform our research to determine which may be the 
most viable measure for a larger controlled trial in the future. 
 
Primary Outcome: SDM (Aim 3). To ascertain effects of GET PrEPD-Psychiatry on SDM, we attempted to 
audio-record each patient’s baseline visit (prior to intervention) and the first visit available after the fourth and 
final coaching session for those who are patients of prescribers who provided consent; however, recording has 
proven to not be feasible for various reasons. Given this is a feasibility study and we have learned that 
collecting recordings this way is not feasible, we have still learned something important from the work we have 
done. We will no longer attempt to record these appointments.  
 
Secondary Outcomes: Self-Management and Recovery (Aim 3). Illness self-management will be 
assessed with the consumer-rated Illness Management and Recovery Scale.114 Items are rated on a 5-point 
behaviorally anchored scale; the mean across all 15 items forms an overall score of illness management, with 
higher scores indicating better self-management. The IMR Scales have shown internal consistency, stability, 
sensitivity to change over time, and correlations with indices of functioning, symptoms, and recovery.115-117  
 
Recovery attitudes will be assessed using the total score of the Brief version of the Recovery Assessment 
Scale.118,119 Respondents endorse 20 items (e.g., "I have a desire to succeed.") on a scale from 1 ("strongly 
disagree") to 5 ("strongly agree"). RAS total score has shown good test-retest reliability, internal consistency, 
and correlates with measures of self-esteem, empowerment, and quality of life.118 
 
Training for Coaches. Clinical psychology doctoral students and/or staff with community mental health 
experience will serve as coaches. Dr. Rand will oversee training, which will consist of didactics, 
demonstrations, and role-plays developed by the research team based on prior experience. Intervention 
sessions will be audio-recorded; however, participants may, at times, decline audio recording an intervention 
session. A random subset of session recordings will be reviewed for fidelity and quality control. On those 
selected sessions, Dr. Rand will complete a coach adherence checklist to help guide feedback during weekly 
group supervision. Role-play will be used to reinforce and correct deviations from study procedures. 
 
Treatment Fidelity. We will use treatment fidelity strategies consistent with the NIH Behavior Change 
Consortium recommendations.104 Strategies will include: (1) using standardized intervention protocols and 
training; (2) monitoring audio-recorded intervention sessions; (3) using coach adherence checklists to track 
3 
Observed 
SDM** 
SDM Scale, rated from 
audiotaped patient-provider visit 
9 
3 
Self-
management 
Illness Management and 
Recovery Scale 
15 
3 
Recovery 
attitudes 
Recovery Assessment Scale 
(brief version) 
20 
*With the exception of acceptability completed at the follow-up visit, 
all measures will be given at baseline and 3-month follow-up. 
**Audio recordings of prescriber appointments have not been 
feasible; therefore, changes in observed SDM will not be analyzed. 
(updated 1/21/2022)  

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protocol deviations; (4) using coach notes to document uptake of intervention; and (5) holding weekly study 
administration meetings to address problems or concerns. 
 
5.0 Study Calendar 
 
  
Screening 
Baseline 
Weekly 
Coaching 
Sessions 1-4 
3-Month 
Follow-up^ 
Informed Consent and Authorization 
X 
  
  
  
Eligibility Checklist 
X 
  
  
  
Demographics, Contact Information 
X 
  
  
  
 
 
 
 
 
PAM-MH 
  
X 
  
X 
ACE Measure 
  
X 
  
X 
PEPPI-5 
  
X 
  
X 
IMR Scale 
  
X 
  
X 
RAS 
  
X 
  
X 
NEII 
  
  
  
X 
Intervention** 
  
  
X 
  
^Activities can occur any time after coaching session 4, within approximately 3 months of Baseline.  
*After completion of coaching intervention, ideally the first prescriber appointment that follows the intervention (time frame could vary). 
**Practice with Virtual Provider Program will be encouraged through the end of the study. 
 
We anticipate each subject’s participation to last approximately 3-4 months; however, this is dependent on 
when their first prescriber appointment following the intervention takes place.   
 
The goal is to complete 4 weekly coaching sessions with each subject and the first session taking place within 
approximately 10 days of baseline data collection. We recognize the need to be flexible with the remaining 
sessions and will allow coaching sessions 2-4 to take place up to approximately 14 days after the previous 
session barring extenuating circumstances. 
 
6.0 Data Management and Analysis Plan 
 
Data Management and Quality Control. The Project Manager and RAs will create and maintain all 
databases. To ensure quality, data will be entered by one team member and checked by another. For 
qualitative data, transcribed interviews and coaching sessions will be checked for accuracy, de-identified, and 
entered in Atlas.ti or other appropriate qualitative data analysis program. The Project Manager and RAs, 
supervised by Dr. Salyers, will be responsible for data management. Each patient will be assigned an ID 
number, and all data will be entered into a database stored behind the University firewall on a secure, HIPAA-
compliant server with limited access to appropriate IU research team members.  
 
Data Analysis. Because inspection of the variable distributions is important for multivariate statistics, 
univariate and multivariate outliers, histograms, probability plots, and residual plots will be examined. We will 
also check the homogeneity of variances/covariances assumption by determining whether the correlations of 
the outcome measures with the covariates and their intercorrelations are approximately the same over time. 
Once we confirm the required assumptions for multivariate statistics, paired Hotelling’s T-square test will be 
used to examine the mean differences of outcomes between baseline and post-intervention. Paired Hotelling’s 
T-square test is used to account for the effect of correlations when there is more than one outcome variable. If 
the omnibus test rejects the null hypothesis, post-hoc tests will be conducted using the “partial” Bonferroni 
correction for potential family-wise type I error rates, which incorporates the mean correlation among 
dependent variables, because the traditional Bonferroni would be too conservative in our correlated data.120 

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Power and sample size. This is a pilot study to test the feasibility and acceptability of GET PrEPD-Psychiatry. 
The primary purpose is not to conduct inferential statistical testing based on p-values.121 Nonetheless, we 
conducted a power analysis given the sample size we plan to obtain in order to facilitate our interpretation of 
the study findings. We are recruiting up to 40 subjects, and we expect minimal attrition based on our prior 
experience working with patients in this clinical setting – thus, our primary power analysis was based on a 
sample size of 40. Nevertheless, we also calculated power for a conservative estimate of 30 participants to 
account for potential loss to follow-up. Basing power on our two target mechanisms (patient activation and 
communication self-efficacy), G*Power 3.0.10 indicated that our study will have .64 to .78 power for the paired 
Hotelling’s T-square test with a sample size of 30 to 40 (.05 alpha, D2=.5 [medium effect size], two measures). 
 
Missing data analysis. We anticipate some missing data, including study dropouts. We will compare patient 
demographic characteristics as well as baseline outcomes between those who withdraw and those who are 
retained to identify characteristics that discriminate dropouts. Further, we will carefully track study participation 
and try to understand reasons for dropout (and missing data) wherever possible. Identifying potential dropout 
(and missing data) mechanisms in this pilot study will allow us to incorporate those factors as auxiliary 
variables for multiple imputation as well as full information maximum likelihood estimation method assuming 
Missing At Random in our future full trial.     
 
7.0 Reportable Events, Suicidality Assessment 
 
Adverse events will not be systematically collected from subjects.  If a research team member becomes aware 
of any adverse events possibly related to study procedures, they will be reported to the Indiana University (IU) 
IRB according to the IU Standard Operating Procedures for Research Involving Human Subjects.  Any 
unanticipated problems involving risk to participants or others will also be reported according to the IU 
Standard Operating Procedures for Research Involving Human Subjects.   
 
Because this is a study to develop a shared decision-making intervention, and we are not targeting depression 
or suicide (or assessing either as a study outcome), we will not directly assess suicidal thoughts. However, we 
recognize that the topic of suicide may come up during a coaching session or during a data collection 
interview. If a participant describes having current thoughts of suicide, hopelessness, or feeling as though they 
would be better off dead during a research activity (either a coaching session or a data collection event), the 
research assistant will proceed to a scripted brief interview (Patient Suicidality Form) to assess risk. If a 
participant is considered high risk (patient answers “yes” to questions 1 and/or 2 on the form), procedures are 
immediately employed by connecting the participant with a qualified mental health professional at SEMHC 
during an in-person visit. SEMHC is a mental health facility that employs psychiatrists, clinical psychologists, 
nurses, and social workers who are qualified and capable of assessing and treating suicidality. In our other 
studies with Eskenazi Health, we have established a similar referral protocol so that participants who express 
suicidal ideation are connected with a clinician during the visit, while onsite. If a patient is assessed to be high 
risk for suicidality during a phone or video call (using the Patient Suicidality Form), the research assistant will 
connect the patient with the Sandra Eskenazi Mental Health Center crisis line and/or other appropriate 
community resource (e.g., emergency department, national suicide hotline). The research assistant will also 
inform appropriate treatment team staff at SEMHC as soon as possible. In addition, the research assistant will 
inform either Dr. Hirsh, Dr. Salyers, or Dr. Rand (all are Clinical Psychologists) as well as the project manager 
immediately following the visit so that it can be ensured that effective safety procedures were followed and 
appropriate reporting is completed. 
 
8.0 Summary 
 
This project addresses the significant public health issues of providing patient-centered care to enhance self-
management and recovery outcomes for people with SMI. As a developmental R34, we are taking the critical 
first step in assessing feasibility and acceptability of an innovative and scalable approach that has the potential 
to efficiently and substantially shift care. GET PrEPD-Psychiatry is characterized by key innovations. Our 
intervention explicitly focuses on patient-level target mechanisms that facilitate self-management and 

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collaborative care. Moreover, the innovative practice sessions with Virtual Providers are tailored to the learning 
needs/preferences of individual patients and allow for frequent skills practice, leading to skills mastery and 
patient self-efficacy. Our approach, informed by our extensive prior work, is scientifically rigorous and 
pragmatic. GET PrEPD-Psychiatry will yield new and important knowledge about collaborative care for people 
with SMI and will be ready for wide dissemination in a variety of clinical settings. The investigators’ expertise, 
track record, and history of collaborating on funded and published research provide strong evidence of our 
ability to complete this project. The scientific and clinical environments are optimal, and there is 
considerable institutional support for the study team. In summary, successful completion of this project will 
result in a new, highly scalable intervention that can be implemented across a range of settings to reach a 
large number of patients, thus having a positive and sustained impact on mental health research and care. 

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Bibliography and References Cited from Grant Proposal 
 
1. 
Dixon LB, Holoshitz Y, Nossel I. Treatment engagement of individuals experiencing mental illness: 
review and update. World Psychiatry. 2016;15(1):13-20.PMC4780300  
2. 
Kreyenbuhl J, Nossel IR, Dixon LB. Disengagement from mental health treatment among individuals 
with schizophrenia and strategies for facilitating connections to care: A review of the literature. 
Schizophrenia Bulletin. 2009;35(4):696-703. 
3. 
Deegan PE. The importance of personal medicine: A qualitative study of resilience in people with 
psychiatric disabilities. Scandinavian Journal of Public Health. 2005;33(66 suppl):29-35. 
4. 
Deegan PE, Drake RE. Shared decision making and medication management in the recovery process. 
Psychiatric Services. 2006;57(11):1636-1639. 
5. 
Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: What does it mean? 
(Or it takes at least two to tango). Social Science and Medicine. 1997;44:681-692. 
6. 
Drake RE, Deegan PE. Shared decision making is an ethical imperative. Psychiatric Services. 
2009;60(8):1007. 
7. 
Ashcroft R, Hope T, Parker M. Ethical issues and evidence-based patient choice. In: Edwards A, Elwyn 
G, eds. Evidence-Based Patient Choice: Inevitable or Impossible? Oxford: Oxford University Press; 
2001:53-65. 
8. 
Joosten E, Defuentes-merillas L, De Weert G, Sensky T, Van Der Staak C, De Jong C. Systematic 
review of the effects of shared decision-making on patient satisfaction, treatment adherence and health 
status. Psychotherapy and Psychosomatics. 2008;77(4):219-226. 
9. 
Institute of Medicine. Crossing the Quality Chasm. Crossing the Quality Chasm: A New Health System 
for the 21st Century. Washington, D.C: National Academy Press; 2001. 
10. 
Drake RE, Deegan PE, Rapp C. The promise of shared decision making in mental health. Psychiatric 
Rehabil Journal. 2010;34(1):7-13. 
11. 
Schauer C, Everett A, del Vecchio P, Anderson L. Promoting the value and practice of shared decision-
making in mental health care. Psychiatric Rehabil Journal. 2007;31(1):54-61. 
12. 
Hamann J, Cohen R, Leucht S, Busch R, Kissling W. Do patients with schizophrenia wish to be 
involved in decisions about their medical treatment? The American Journal of Psychiatry. 
2005;162(12):2382-2384. 
13. 
Morant N, Kaminskiy E, Ramon S. Shared decision making for psychiatric medication management: 
Beyond the micro‐social. Health Expectations: An International Journal of Public Participation in Health 
Care & Health Policy. 2016;19(5):1002-1014. 
14. 
Gravel K, Légaré F, Graham ID. Barriers and facilitators to implementing shared decision-making in 
clinical practice: a systematic review of health professionals' perceptions. Implementation Science. 
2006;1:1-16. 
15. 
Karnieli-Miller O, Salyers MP. Clinical communicatons with persons who have severe mental illnesses. 
In: Rudnick A, Roe D, eds. Serious Mental Illness: Person-Centered Approaches. London: Radcliffe 
2011. 
16. 
Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? 
Effective Clinical Practice. 1998;1:2-4. 
17. 
Bodenheimer T, Lorig K, Holman H, Grumbach K. Patient self-management of chronic disease in 
primary care. JAMA. 2002;288(19):2469-2475. 
18. 
Von Korff M, Gruman J, Schaefer J, Curry SJ, Wagner EH. Collaborative Management of Chronic 
Illness. Annals of Internal Medicine. 1997;127:1097-1102. 
19. 
Wagner EH, Bennett SM, Austin BT, Greene SM, Schaefer JK, Vonkorff M. Finding common ground: 
patient-centeredness and evidence-based chronic illness care. Third American Samueli Symposium, 
Developing Healing Relationships, April 21-22, 2005. Journal of Alternative & Complementary 
Medicine. 2005;11:S7-S15. 
20. 
Michie S, Miles J, Weinman J. Patient-centredness in chronic illness: what is it and does it matter?[see 
comment]. Patient Education & Counseling. 2003;51(3):197-206. 
21. 
Hibbard JH, Cunningham PJ. How engaged are consumers in their health and health care, and why 
does it matter? Research Brief. 2008;8:1-9. 

Protocol 
IRB #2010326129 
Version: 05/03/2022 
 
 
15 
22. 
Hibbard JH, Mahoney ER, Stock R, Tusler M. Do increases in patient activation result in improved self-
management behaviors? Health Services Research. 2007;42(4):1443-1463. 
23. 
Matthias MS, Fukui S, Salyers MP. What factors are associated with consumer initiation of shared 
decision making in mental health visits? Administration and Policy in Mental Health and Mental Health 
Services Research. 2017;44(1):133-140.PMC4818194. 
24. 
Baumeister RF, Gailliot M, DeWall NC, Oaten M. Self-regulation and personality: how interventions 
increase regulatory success, and how depletion moderates the effects of traits on behavior. Journal of 
Personality  2006;74(6):1773-1802. 
25. 
Carver CS, Scheier MF. Attention and self regulation: A control theory to human behavior. New York: 
Springer-Verlag; 1981. 
26. 
Bauer SM, McGuire AB, Kukla M, McGuire S, Bair MJ, Matthias MS. Veterans' pain management 
goals: changes during the course of a peer-led pain self-management program. Patient Education and 
Counseling. in press. 
27. 
Sergerstrom SC, Nes LS. When goals conflict, but people prosper: the case of dispositional optimism. 
Journal of Research in Personality. 2006;40:675-693. 
28. 
Deegan PE, Rapp C, Holter M, Riefer M. Best practices: A program to support shared decision making 
in an outpatient psychiatric medication clinic. Psychiatric Services. 2008;59(6):603-605. 
29. 
Deegan PE. A web application to support recovery and shared decision making in psychiatric 
medication clinics. Psychiatric Rehabil Journal. 2010;34(1):23-28. 
30. 
2013 APA Gold Award: Amplifying the voices of individuals who use mental health services: A 
commitment to shared decision making. Psychiatric Services. 2013;64(10):e7-e9. 
31. 
MacDonald-Wilson KL, Deegan PE, Hutchison SL, Parrotta N, Schuster JM. Integrating personal 
medicine into service delivery: Empowering people in recovery. Psychiatric Rehabil Journal. 
2013;36(4):258-263. 
32. 
Stein BD, Kogan JN, Mihalyo MJ, et al. Use of a computerized medication shared decision making tool 
in community mental health settings: Impact on psychotropic medication adherence. Community Mental 
Health Journal. 2013;49(2):185-192. 
33. 
Salyers MP, Fukui S, Bonfils KA, et al. Consumer outcomes after implementing CommonGround as an 
approach to shared decision making. Psychiatric Services. 2017;68(3):299-302.PMC5658777. 
34. 
Bonfils KA, Dreison KC, Luther L, et al. Implementing CommonGround in a community mental health 
center: Lessons in a computerized decision support system. Psychiatric Rehabil Journal. 2016, October 
10;Advance online publication. 
35. 
Luther L, Gearhart T, Fukui S, Morse G, Rollins AL, Salyers MP. Working overtime in community 
mental health: Associations with clinician burnout and perceived quality of care. Psychiatric Rehabil 
Journal. 2017;40(2):252-259.PMC5574255. 
36. 
Morse G, Salyers M, Rollins A, Monroe-DeVita M, Pfahler C. Burnout in mental health services: A 
review of the problem and its remediation. Administration and Policy in Mental Health and Mental 
Health Services Research. 2012;39(5):341-352. PMC3156844. 
37. 
Greenfield S, Kaplan S, Ware JE, Jr. Expanding patient involvement in care. Effects on patient 
outcomes. Annals of Internal Medicine. 1985;102(4):520-528. 
38. 
Hamann J, Maris N, Iosifidou P, et al. Effects of a question prompt sheet on active patient behaviour: A 
randomized controlled trial with depressed outpatients. International Journal of Social Psychiatry. 
2014;60(3):227-235. 
39. 
Sansoni JE, Grootemaat P, Duncan C. Question prompt lists in health consultations: A review. Patient 
Education and Counseling. 2015;98(12):1454-1464. 
40. 
Alegría M, Polo A, Gao S, et al. Evaluation of a patient activation and empowerment intervention in 
mental health care. Medical Care. 2008;46(3):247-256. 
41. 
Alegría M, Carson N, Flores M, et al. Activation, self-management, engagement, and retention in 
behavioral health care: A randomized clinical trial of the DECIDE intervention. JAMA Psychiatry. 
2014;71(5):557-565. 
42. 
Hamann J, Mendel R, Meier A, et al. 'How to speak to your psychiatrist': Shared decision-making 
training for inpatients with schizophrenia. Psychiatric Services. 2011;62(10):1218-1221. 
43. 
Hibbard JH, Eldon RM, Jean S, Martin T. Development and testing of a short form of the patient 
activation measure. Health Services Research. 2005;40(6):1918-1930. 

Protocol 
IRB #2010326129 
Version: 05/03/2022 
 
 
16 
44. 
Eliacin J, Coffing JM, Matthias MS, Burgess DJ, Bair MJ, Rollins AL. The relationship between race, 
patient activation, and working alliance: implications for patient engagement in mental health care. 
under review. 
45. 
Matthias MS, Fukui S, Kukla M, et al. Consumer and relationship factors associated with shared 
decision making in mental health consultations. Psychiatric Services. 2014;65(12):1488-1491. 
46. 
Charles C, Gafni A, Whelan T. Decision-making in the physician–patient encounter: Revisiting the 
shared treatment decision-making model. Social Science & Medicine. 1999;49(5):651-661. 
47. 
McGee DS, Cegala DJ. Patient communication skills training for improved communication competence 
in the primary care medical consultation. Journal of Applied Communication Research. 1998;26(4):412-
430. 
48. 
ten Klooster PM, Oostveen JC, Zandbelt LC, et al. Further validation of the 5-item Perceived Efficacy in 
Patient-Physician Interactions (PEPPI-5) scale in patients with osteoarthritis. Patient Education and 
Counseling. 2012;87(1):125-130. 
49. 
Frankel RM, Salyers MP, Bonfils K, Oles S, Matthias MS. Agenda Setting in Psychiatric Consultations: 
An Exploratory Study. Psychiatric Rehabil Journal. 2013;36(3):195-201.PMC4023682. 
50. 
Matthias MS, Salyers MP, Frankel RM. Re-thinking shared decision-making: Context matters. Patient 
Education and Counseling. 2013;91(2):176-179.PMID: 23410979  
51. 
Polo AJ, Alegría M, Sirkin JT. Increasing the engagement of Latinos in services through community-
derived programs: The Right Question Project–Mental Health. Professional Psychology: Research and 
Practice. 2012;43(3):208-216. 
52. 
Montori VM, Gafni A, Charles C. A shared treatment decision-making approach between patients with 
chronic conditions and their clinicians: the case of diabetes. Health Expectations. 2006;9(1):25-36. 
53. 
Bartley EJ, Boissoneault J, Vargovich AM, et al. The influence of healthcare professional characteristics 
on pain management decisions. Pain Medicine. 2015;1i6:99-111.PMC5555370. 
54. 
Boissoneault J, Mundt JM, Bartley EJ, Wandner LD, Hirsh AT, Robinson ME. Assessment of the 
influence of demographic and professional characteristics on health care providers’ pain management 
decisions using virtual humans. Journal of Dental Education. 2016;80:578-587.PMC4899979. 
55. 
Torres CA, Bartley EJ, Wandner LD, Alqudah A, Hirsh AT, Robinson ME. The influence of sex, race, 
and age on pain assessment and treatment decisions using virtual human technology: a cross-national 
comparison. Journal of Pain Research. 2013;6:577-588.PMC3724685. 
56. 
Wandner LD, Heft MW, Lok B, et al. The impact of patients' gender, race, and age on health care 
professionals' pain management decisions: an online survey using virtual human technology. 
International Journal of Nursing Studies. 2014;51:726-733.PMC3969773. 
57. 
Wandner LD, Hirsh AT, Torres CA, et al. Using virtual human technology to capture dentists’ decision 
policies about pain. Journal of Dental Research. 2013;92:301-305.PMC3706173. 
58. 
Wandner LD, Stutts LA, Alqudah A, et al. Virtual human technology: patient demographics and 
healthcare training factors in pain observation and treatment recommendations. Journal of Pain 
Research. 2010;3:241-247.PMC3033032. 
59. 
Hirsh AT, Callander SB, Robinson ME. Patient demographic characteristics and facial expressions 
influence nurses’ assessment of mood in the context of pain: a Virtual Human and lens model 
investigation. International Journal of Nursing Studies. 2011;48:1330-1338.PMC3170511. 
60. 
Hirsh AT, George SZ, Robinson ME. Pain assessment and treatment disparities: a Virtual Human 
technology investigation. Pain. 2009;143:106-113.PMC2666785. 
61. 
Hirsh AT, Hollingshead NA, Ashburn-Nardo L, Kroenke K. The interaction of patient race, provider bias, 
and clinical ambiguity on pain management decisions. The Journal of Pain. 2015;16:558-
568.PMC4456233. 
62. 
Hirsh AT, Hollingshead NA, Bair MJ, Matthias MS, Kroenke K. Preferences, experience, and attitudes 
in the management of chronic pain and depression: a comparison of physicians and medical students. 
Clinical Journal of Pain. 2014;30:766-774.PMID: 24064934. 
63. 
Hirsh AT, Hollingshead NA, Bair MJ, Matthias MS, Kroenke K. The influence of patient sex, provider 
sex, and sexist attitudes on pain treatment decisions. The Journal of Pain. 2014;15:551-559.PMID: 
24576430. 

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64. 
Hirsh AT, Hollingshead NA, Bair MJ, Matthias MS, Wu J, Kroenke K. The influence of patient sex, race, 
and depression on clinician pain treatment decisions. European Journal of Pain. 2013;17:1569-
1579.PMID: 23813861. 
65. 
Alqudah AF, Hirsh AT, Stutts LA, Scipio CD, Robinson ME. Sex and race differences in rating others’ 
pain, pain-related negative mood, pain coping, and recommending medical help. Journal of 
Cybertherapy and Rehabilitation. 2010;3:63-70.PMC3077073. 
66. 
Hollingshead NA, Matthias MS, Bair MJ, Hirsh AT. Impact of race and sex on pain management by 
medical trainees: a mixed-methods pilot study of decision-making and awareness of influence. Pain 
Medicine. 2015;16:280-290.PMID: 25039974. 
67. 
Greene R. Repetition and learning. In: Byrne JH, ed. Learning and memory. New York: MacMillan 
Reference; 2003. 
68. 
Schunk DH. Information processing theory. In: Learning theories. Boston, MA: Pearson Education, Inc.; 
2012:163-228. 
69. 
Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored prinnt health 
behavior change interventions. Psychological Bulletin. 2007;133(4):673. 
70. 
Stevens VJ, Hollis JF. Preventing smoking relapse using an individually tailored skills-training 
technique. J Consult Clin Psych. 1989;57(3):420-424. 
71. 
Greene DR, Baddeley AD. Context-dependent memory in two natural environments: on land and under 
water. British Journal of Psychology. 1975;66(3):325-331. 
72. 
Kitchenham A. The Evolution of John Mezirow's Transformative Learning Theory. Journal of 
Transformative Education. 2008;6(2):104-123. 
73. 
Godden DR, Baddeley AD. Context-dependent memory in two natural environments: on land and under 
water. British Journal of Psychology. 1975;66(3):325-331. 
74. 
Mueser KT, Meyer PS, Penn DL, Clancy R, Clancy DM, Salyers MP. The Illness Management and 
Recovery program: Rationale, development, and preliminary findings. Schizophrenia Bulletin. 
2006;32(1):32-43.PMC2685197. 
75. 
Salyers MP, McGuire AB, Kukla M, Fukui S, Lysaker PH, Mueser KT. A randomized controlled trial of 
illness management and recovery with an active control group. Psychiatric Services. 2014;65(8):1005-
1011.PMID: 24733680. 
76. 
Salyers MP, Godfrey JL, McGuire AB, Gearhart T, Rollins AL, Boyle C. Implementing the illness 
management and recovery program for consumers with severe mental illness. Psychiatric Services. 
2009;60(4):483-490.PMID: 19339323  
77. 
Salyers MP, McGuire AB, Rollins AL, Bond GB, Mueser KT, Macy V. Integrating Assertive Community 
Treatment and Illness Management and Recovery for consumers with severe mental illness. 
Community Mental Health Journal. 2010;46(4):319-329.PMID: 20077006  
78. 
Salyers MP, Hicks LJ, McGuire AB, Baumgardner H, Ring K, Kim HW. A pilot to enhance the recovery 
orientation of assertive community treatment through peer provided illness management and recovery. 
American Journal of Psychiatric Rehabilitation. 2009;12(3):191-204. 
79. 
Kukla M, Salyers MP, Lysaker PH. Levels of patient activation among adults with schizophrenia: 
Associations with hope, symptoms, medication adherence, and recovery attitudes. Journal of Nervous 
& Mental Disease. 2013;201(4):339-344.PMID: 23538980  
80. 
Salyers MP, Matthias MS, Fukui S, et al. A Coding System to Measure Elements of Shared Decision 
Making During Psychiatric Visits. Psychiatric Services 2012;63(8):779-784.PMC3982790. 
81. 
Salyers MP, Matthias MS, Spann C, Lydick J, Rollins AL, Frankel RM. The role of patient activation in 
psychiatric visits. Psychiatric Services. 2009;60(11):1535-1539.PMC2777630. 
82. 
Fukui S, Matthias MS, Salyers MP. Core domains of shared decision-making during psychiatric visits: 
Scientific and preference-based discussions. Administration and Policy in Mental Health and Mental 
Health Services Research. 2015;42(1):40-46.PMC4125549. 
83. 
Fukui S, Salyers MP, Matthias MS, et al. Predictors of shared decision making and level of agreement 
between consumers and providers in psychiatric care. Community Mental Health Journal. 2013:1-
8.PMC3980460. 
84. 
Hollingshead NA, Meints SM, Middleton SK, Free CA, Hirsh AT. Examining influential factors in 
providers' chronic pain treatment decisions: a comparison of physicians and medical students. BMC 
medical education. 2015;15:164.PMC4591590. 

Protocol 
IRB #2010326129 
Version: 05/03/2022 
 
 
18 
85. 
Hirsh AT, Jensen MP, Robinson ME. Evaluation of nurses' self-insight into their pain assessment and 
treatment decisions. The Journal of Pain. 2010;11:454-461.PMC2864339. 
86. 
Stutts LA, Hirsh AT, George SZ, Robinson ME. Investigating patient characteristics on pain assessment 
using virtual human technology. European Journal of Pain. 2010;14:1040-1045.PMC2980573. 
87. 
White C, Wendling A, Chuah J, et al. Using a critical incident scenario with virtual humans to assess 
educational needs of nurses in a post-anesthesia care unit. Journal for Continuing Education in Health 
Professions. 2015;35(3):158-165.PMID: 26378421. 
88. 
Robb A, White C, Cordar A, Wendling A, Lampotang S, Lok B. A comparison of speaking up behavior 
during conflict with real and virtual humans. Computers in Human Behavior. 2015;52:12-21. 
89. 
Cordar A, Robb A, Wendling A, Lampotang S, White C, Lok B. Virtual Role-Models: Using Virtual 
Humans to Train Best Communication Practices for Healthcare Teams (short paper). Intelligent Virtual 
Agents. 2015:229-238. 
90. 
Robb A, White C, Cortina JM, Wendling A, Lampotang S, Lok B. A qualitative evaluation of behavior 
during conflict with an authoritative virtual human. Intelligent Virtual Agents. 2014:397-409. 
91. 
Johnsen K, Raij A, Stevens A, Lind DS, Lok B. The validity of a virtual human experience for 
interpersonal skills education. Paper presented at: Proceedings of the SIGCHI Conference on Human 
Factors in Computing Systems2006; Montreal, CA, April 22-27, 2006. 
92. 
Raij A, Johnsen K, Dickerson R, et al. Interpersonal scenarios: virtual approx real? . Paper presented 
at: Virtual Reality Conference2006; Alexandria, VA, March 25-29, 2006. 
93. 
Mosher CE, Daily S, Tometich D, et al. Factors underlying metastatic breast cancer patients' 
perceptions of symptom importance: a qualitative analysis. European Journal of Cancer Care. in 
press:1-6.PMC5272918. 
94. 
Mosher CE, Tometich D, Hirsh AT, et al. Symptom experiences in metastatic breast cancer patients: 
relationships to activity engagement, value-based living, and psychological inflexibility. Psycho-
Oncology. 2016;2017(26):1944-1951.PMC5359070. 
95. 
Hirsh AT, Atchison JW, Berger JJ, et al. Patient satisfaction with treatment for chronic pain: predictors 
and relationship to compliance. Clinical Journal of Pain. 2005;21:302-310.PMID: 15951647. 
96. 
George SZ, Hirsh AT. Distinguishing patient satisfaction with treatment delivery from treatment effect: a 
preliminary investigation of patient satisfaction with symptoms following physical therapy treatment of 
low back pain. Archives of Physical Medicine and Rehabilitation. 2005;86:1338-1344.PMID: 16003661. 
97. 
Robinson ME, Brown JL, George SZ, et al. Multidimensional success criteria and expectations for 
treatment of chronic pain: the patient perspective. Pain Medicine. 2005;6:336-345.PMID: 16266354. 
98. 
Robinson ME, Bulcourf BB, Atchison JW, et al. Compliance in pain rehabilitation: patient and provider 
perspectives. Pain Medicine. 2004;5:66-80.PMID: 14996239. 
99. 
Cottingham AH, Cripe LD, Rand KL, Frankel RM. “My future is now”: A qualitative study of persons 
living with advanced cancer. American Journal of Hospice and Palliative Medicine. 2017.PMID: 
28992715. 
100. 
Rand KL, Banno DA, Shea AM, Cripe LD. Life and treatment goals of patients with advanced, incurable 
cancer. Supportive Care in Cancer. 2016;24:2953-2962.PMID: 26864986. 
101. 
Rand KL, Cripe LD, Monahan PO, Tong Y, Schmidt K, Rawl SM. Illness appraisal, religious coping, and 
psychological responses in men with advanced cancer. Supportive Care in Cancer. 2012;20:1719-
1728.PMID: 21927993. 
102. 
Klein G, Militello L. Some guidelines for conducting a cognitive task analysis. In: Advances in human 
performance and cognitive engineering research. Emerald Group Publishing Limited; 2001:163-199. 
103. 
Hsieh HF, Shannon SE. Three approaches to qualitative content analysis. Qual Health Res. 
2005;15(9):1277-1288. 
104. 
Bellg AJ, Borrelli B, Resnick B, et al. Enhancing treatment fidelity in health behavior change studies: 
best practices and recommendations from the NIH Behavior Change Consortium. Health Psychology. 
2004;23:443-451. 
105. 
Hasson-Ohayon I, Roe D, Kravetz S. A qualitative approach to the evaluation of psychosocial 
interventions for persons with severe mental illness. Psychological Services. 2006;3(4):262–273. 
106. 
Kukla M, Strasburger AM, Salyers MP, Rattray NA, Lysaker PH. Subjective experiences of the benefits 
and key elements of a cognitive behavioral intervention focused on community work outcomes in 
persons with mental illness. Journal of Nervous & Mental Disease. 2017;205(1):66-73.PMID: 27741081  

Protocol 
IRB #2010326129 
Version: 05/03/2022 
 
 
19 
107. 
Roe D, Hasson-Ohayon I, Salyers MP, Kravetz S. A one year follow-up of Illness Management and 
Recovery: Participants' accounts of its impact and uniqueness. Psychiatric Rehabil Journal. 
2009;32(4):285-291.PMID: 19346207  
108. 
Green C, Perrin N, Polen M, Leo M, Hibbard J, Tusler M. Development of the Patient Activation 
Measure for Mental Health. Administration and Policy in Mental Health and Mental Health Services 
Research. 2010;37(4):327-333. 
109. 
Salyers MP, Matthias MS, Sidenbender S, Green A. Patient activation in schizophrenia: insights from 
stories of illness and recovery. Administration and Policy in Mental Health and Mental Health Services 
Research. 2013;40(5):419-427.PMID: 22936284. 
110. 
Chinman M, Oberman R, Hanusa B, et al. A cluster randomized trial of adding peer specialists to 
intensive case management teams in the veterans health administration. The Journal of Behavioral 
Health Services & Research. 2013:1-13.PMC5649383. 
111. 
Matthias MS, McGuire AB, Kukla M, Daggy J, Myers LJ, Bair MJ. A brief peer support intervention for 
veterans with chronic pain: a pilot study of feasibility and effectiveness. Pain Medicine. 2015;16(1):81-
87. 
112. 
Bartels SJ, Aschbrenner KA, Rolin SA, Hendrick DC, Naslund JA, Faber MJ. Activating older adults 
with serious mental illness for collaborative primary care visits. Psychiatric Rehabil Journal. 
2013;36(4):278-288. 
113. 
Braddock CH, Fihn, S. D., Levinson, W., Jonsen,A. R., & Pearlman, R. A. How doctors and patients 
discuss routine clinical decisions: Informed decision making in the outpatient setting. Journal of General 
Internal Medicine. 1997;12:339-345. 
114. 
Mueser KT, Gingerich S, Salyers MP, McGuire AB, Reyes RU, Cunningham H. The Illness 
Management and Recovery Scale (IMR). New Hampshire-Dartmouth Psychiatric Research 
Center;2004. 
115. 
Fardig R, Lewander T, Fredriksson A, Melin L. Evaluation of the Illness Management and Recovery 
Scale in schizophrenia and schizoaffective disorder. Schizophr Res. 2011;132(2-3):157-164. 
116. 
Hasson-Ohayon I, Roe D, Kravetz S. The psychometric properties of the illness management and 
recovery scale: Client and clinician versions. Psychiatry Research. 2008;160(2):228-235. 
117. 
Salyers MP, Godfrey JL, Mueser KT, Labriola S. Measuring illness management outcomes: a 
psychometric study of clinician and consumer rating scales for illness self management and recovery. 
Community Mental Health Journal. 2007;43(5):459-480.PMID: 17514504. 
118. 
Roe D, Mashiach-Eizenberg M, Corrigan P. Confirmatory Factor Analysis of the Brief Version of the 
Recovery Assessment Scale. The Journal of Nervous and Mental Disease. 2012;200(10):847-851. 
119. 
Corrigan PW, Giffort D, Rashid F, Leary M, Okeke I. Recovery as a psychological construct. 
Community Mental Health Journal. 1999;35(3):231-239. 
120. 
Sankoh AJ, Huque MF, Dubey SD. Some comments on frequently used multiple endpoint adjustment 
methods in clinical trials. Statistics in Medicine. 1997;16(22):2529-2542. 
121. 
Leon AC, Davis LL, Kraemer HC. The role and interpretation of pilot studies in clinical research. Journal 
of Psychiatric Research. 2011;45(5):626-629. 
122. 
Priebe S, McCabe R, Bullenkamp J, et al. Structured patient-clinician communication and 1-year 
outcome in community mental healthcare: cluster randomised controlled trial. British Journal of 
Psychiatry. 2007;191:420-426. 
123. 
Young HN, Bell RA, Epstein RM, Feldman MD, Kravitz RL. Physicians' Shared Decision-Making 
Behaviors in Depression Care. Archives of Internal Medicine. 2008;168(13):1404-1408. 
124. 
King JS, Moulton BW. Re-thinking informed consent: the case for shared medical decision making. 
American Journal of Law and Medicine. 2006;32:429-501. 
125. 
Dreison K, Luther L, Bonfils KA, McGrew JH, Sliter MT, Salyers MP. Job burnout in mental health 
providers: A meta-analysis of 35 years of intervention research. Journal of Occupational Health 
Psychology. 2018;23:18-30.PMID: 27643608  
126. 
Kalauokalani D, Franks P, Oliver JW, Meyers FJ, Kravitz RL. Can patient coaching reduce racial/ethnic 
disparities in cancer pain control? Secondary analysis of a randomized controlled trial. Pain Medicine. 
2007;8(1):17-24. 

Protocol 
IRB #2010326129 
Version: 05/03/2022 
 
 
20 
127. 
Greenfield S, Kaplan SH, Ware JE, Yano EM, Frank HJL. Patients' participation in medical care: Effects 
on blood sugar control and quality of life in diabetes. Journal of General Internal Medicine. 1988;3:448-
457. 
128. 
Rost KM, Flavin KS, Cole K, McGill JB. Change in metabolic control and functional status after 
hospitalization. Impact of patient activation intervention in diabetic patients. Diabetes Care. 
1991;14:881-889. 
129. 
Firth J, Torous J. Smartphone apps for schizophrenia: A systematic review. JMIR Mhealth Uhealth. 
2015;3(4):e102. 
130. 
Donker T, Petrie K, Proudfoot J, Clarke J, Birch M-R, Christensen H. Smartphones for smarter delivery 
of mental health programs: A systematic review. Journal of Medical Internet Research. 
2013;15(11):239-251. 
131. 
Alvarez-Jimenez M, Alcazar-Corcoles MA, González-Blanch C, Bendall S, McGorry PD, Gleeson JF. 
Online, social media and mobile technologies for psychosis treatment: A systematic review on novel 
user-led interventions. Schizophrenia Research. 2014;156(1):96-106.