Brief Intervention to Increase COVID-19 Knowledge in People With Serious Mental Illness
Phase: NA
Conditions: Mental Illness, Chronic Disease
Sponsor: Dartmouth-Hitchcock Medical Center

V 03 8 2022
1
Dartmouth College • Dartmouth-Hitchcock Medical Center
COMMITTEE FOR THE PROTECTION OF HUMAN SUBJECTS
CPHS.Tasks@Dartmouth.edu • 603-646-6482
SOCIAL, BEHAVIORAL, and NON-CLINICAL RESEARCH PLAN
Please complete: CPHS#31245
PI: Sarah Pratt, PhD

Respond to each item, even if to indicate N/A or not applicable

Attach and/or upload this form as your ‘Investigator Protocol’ in Rapport

If you are completing this form on a Mac, indicate your answer to any checkboxes by 
bolding or highlighting, or by deleting any incorrect options.
Comparing Integrated Illness Management and Recovery to the Stanford Chronic Disease Self-
Management Program in People with Serious Mental Illness
1. Introduction and Background
People with serious mental illness (SMI), including schizophrenia, schizoaffective disorder, bipolar 
disorder and chronic depression comprise up to 4-6% of the population1 and experience one of the 
nation’s greatest health disparities with a life expectancy that is 11-30 years shorter than the general 
population.2-5 This alarming health disparity is largely due to disproportionately high rates of 
cardiovascular disease, diabetes, chronic obstructive pulmonary disease, obesity, and tobacco use.6 In 
2007, consumer advocates, federal agencies, provider organizations, and researchers (including 
Stephen Bartels, Co-PI for this proposal) came together to launch the “10x10 Campaign”, with the goal 
to “increase the life expectancy of people with serious mental illness by 10 years in 10 years.”7 A 
decade later it is tragically clear that we have collectively failed to deliver on this commitment to 
consumers and their families. Although substantial progress has been made in developing evidence-
based practices to improve health outcomes for people with SMI8-15, there has been little change in the 
high rates of chronic health conditions, excess disability, and early mortality.16-19 In response to this 
major health disparity, a national demonstration program by the Substance Abuse and Mental Health 
Administration (SAMHSA) supported broad dissemination of “behavioral health homes” for people 
with SMI. This initiative has resulted in increased rates of screening and treatment of chronic disease, 
but has not significantly improved key health outcomes 16,18,20. It has become clear that improving 
screening and access to primary care is necessary, but not sufficient to substantively improve health 
outcomes and the life expectancy of people with SMI. Consumer advocates, provider organizations, 
policymakers, and researchers now concur that illness self-management programs are essential to 
improving health outcomes and in reducing early mortality risk for people with SMI. As a reflection of 
this clear consensus on the critical role of self-management, SAMHSA now requires that mental health 
providers offer self-management programs as a core component of all behavioral health homes.
The proposed study will be the first to compare two commonly used but substantially different, 
evidence-based self-management interventions. Integrated Illness Management and Recovery (I-
IMR)9,11 is an individually-tailored, 16-session, integrated program combining both physical and 
mental health self-management specifically developed for people with SMI. In contrast, the Stanford 
Chronic Disease Self-Management Program (CDSMP)21,22 is a group-based, 6-session, chronic 

V 03 8 2022
2
disease self-management program largely focused on physical health self-management alone. I-IMR 
is delivered by community mental health providers or by community outreach workers, while CDSMP 
is co-delivered by two peers or by a health professional and a peer. Both programs have been widely 
recommended, disseminated, and used.
While life expectancy for the general population has increased each decade since the 1970s, numerous 
studies across the US have shown that it has decreased for those with SMI,19 standing at an alarming 
11 to 30 years less for people with SMI such as schizophrenia, bipolar disorder, or chronic 
depression.3-5 This represents one of the greatest health disparities in the nation. The prevalence of 
chronic illness in people with SMI dramatically exceeds that of the general population, including 
cardiovascular disease,4,6,23 diabetes, chronic obstructive pulmonary disease, and other diseases.6,24-30 
Cardiovascular disease is the major cause of early mortality for this high-risk group, due to high rates 
of obesity, diabetes, hypertension, sedentary behavior, poor nutrition, and smoking. SMI is not only 
devastating for individuals and families, through its impact on functioning, productivity, and early 
mortality, it also exerts considerable burden on the US population and economy. People with SMI and 
medical comorbidity have costs that are 2-3 times greater than those of people with medical illness 
alone.31,32 The greater incidence of medical comorbidity,4,6,23-30,33 early mortality,2-5 and 
disproportionately high costs among people with SMI make this group a major priority for developing 
and disseminating effective and sustainable models of integrated Illness self-management. Knowledge 
generated from studies of people with SMI may also inform initiatives directed at other challenging, 
health disparity populations.
This population and their social vulnerabilities also place them at disproportionate risk for COVID-19 
acquisition, morbidity, and mortality.117-118 Many social vulnerabilities119 also increase risk of negative 
COVID-19 outcomes, including poverty, homelessness, and residence in congregate and transitional 
group homes, emergency shelters, and long-term care facilities where viral transmission has happened 
rapidly. Without specific training on COVID-19, these vulnerabilities will continue to be a source of 
unchecked community transmission that will have larger societal impacts.
We received funding to develop and evaluate the effectiveness of a 3-session I-IMR module to provide 
specific training on how to prevent contracting and spreading COVID-19 and other acute viral 
infectious diseases that may arise in the future and will similarly require: 1) knowledge and enactment 
of measures to maximize personal and societal safety (e.g., safely acquiring food, paying bills, 
accessing healthcare and COVID-related resources), 2) use of illness self-management behaviors to 
reduce risk of contracting and spreading illness, and 3) methods for addressing social determinants of 
health (e.g., maintaining social connection while ensuring physical distancing).
This component of the project will involve: 1) an initial needs assessment that will inform the 
refinement and specification of the training materials in the 3-session I-IMR COVID-19 and viral 
infectious disease module, 2) delivery of the module in phone or video-based sessions with a clinician 
trained in I-IMR, and 3) evaluation of the impact of the module on key outcomes. I-IMR participants 
will receive this 3-session module, which will be individually-delivered by phone or video (e.g., secure 
Zoom) to 75 participants (current and future participants assigned to I-IMR).  We are not at liberty to 
add training on infectious viral diseases to CDSMP because this is not our intervention. The CDSMP 
participants are essential to the study as they serve as the comparison group, preserving the 
comparative effectiveness design, as required by PCORI.  
2. Objectives and Hypotheses

V 03 8 2022
3
This study will compare two commonly used but substantially different, evidence-based self-
management interventions. Integrated Illness Management and Recovery (I-IMR)9,11 is an 
individually-tailored, 16-session, integrated program combining both physical and mental health self-
management specifically developed for people with SMI. In contrast, the Stanford Chronic Disease 
Self-Management Program (CDSMP)21,22 is a group-based, 6-session, chronic disease self-
management program largely focused on physical health self-management alone. I-IMR is delivered 
by community mental health providers or by community outreach workers, while CDSMP is co-
delivered by two peers or by a health professional and a peer. 
 In response to the COVID 19 pandemic, starting on March 13, 2020, we pivoted in-person delivery of  
all study group interventions and assessments to phone or video conference (Zoom).  Beginning in July 
2021, one of our sites (TN) will resume in-person services, so groups will revert to in-person (with 
remote assessments continuing for the time being).  The other site has no established timeline for 
resuming in-person services, but we anticipate this happening at some point in the future, before the 
end of the study period. Given the unpredictable nature of the pandemic and our established experience 
and procedures for delivering the groups and assessments in-person or remotely, we plan to include 
language in the consent form explaining that the mode of delivery will depend on the status of the 
pandemic and whether services are being provided remotely or in-person.  This will allow us to nimbly 
shift between remote and in-person as needed when and if abrupt changes related to the pandemic 
occur at the study sites, thus minimizing any interruption in delivery of study activities.  For remote 
groups, participants will receive the intervention handouts in advance by mail so that they can read 
from and refer to them as they would during an in-person session.  They will also receive a handout 
with instructions for successfully participating in research assessments and joining groups remotely. 
The same group leaders trained to deliver the interventions in person will deliver them remotely. 
Supervisors at Dartmouth will review video-recordings of remote groups whereas in-person groups 
will be audiotaped only for fidelity monitoring purposes. 
The proposed study will randomize people with serious mental illness to I-IMR (n=300) and CDSMP 
(n=300) to address the following four specific aims and hypotheses:
Aim 1: Compare the effectiveness of I-IMR to CDSMP with respect to our primary physical health 
self-management outcomes.
Hypothesis 1: I-IMR compared to CDSMP will be associated with greater improvements in physical 
health self-management abilities (Self-Rated Abilities for Health Practices scale; SRAHP, and a 
qualitative interview) and physical health consumer activation (Patient Activation Measure; PAM); 
and (secondary hypothesis) greater improvement in self-reported physical health functioning (PROMIS 
Global Health measure).
Aim 2: Compare the effectiveness of I-IMR to CDSMP with respect to our primary mental health 
self-management outcomes.
Hypothesis 2: I-IMR compared to CDSMP will be associated with greater improvements in mental 
health self-management abilities (Illness Management and Recovery Scale; IMRS, and a qualitative 
interview); and mental health consumer activation (PAM-MH); and (secondary hypothesis) greater 
improvement in self-reported mental health (PROMIS Global Health measure).
Aim 3: Compare the effectiveness of I-IMR to CDSMP with respect to emergency room and acute 
care hospital days, and risk of 10-year cardiovascular mortality.
Hypothesis 3: Participation in I-IMR compared to CDSMP will be associated with fewer emergency 
room admissions and acute care hospital days over 12 months compared to the 12 months before 
participation; and greater reduction in 10-year cardiovascular risk score (ACC/AHA Cardiovascular 
Risk Score) from baseline to 12 months.

V 03 8 2022
4
Aim 4: Compare I-IMR to CDSMP with respect to subgroup differences in self-management outcomes 
by mental illness subgroup (schizophrenia-spectrum vs. mood disorders) and by age group (18-49 vs. 
50+). 
Hypothesis 4: Comparing I-IMR and CDSMP, the difference in treatment effects will be greater for 
people with schizophrenia spectrum disorders compared to mood disorders; and greater for older (age 
50+) compared to younger (18-49) participants with respect to: physical health self-management 
abilities and activation (SRAHP; PAM); mental health self-management abilities and activation 
(IMRS; PAM-MH); and 10-year cardiovascular risk reduction (ACC/AHA Cardiovascular Risk 
Score).
COVID-19 Enhancement Primary Aim: After co-design of the 3-session I-IMR module, we will 
evaluate the impact of the module by comparing participants assigned to I-IMR (n=75) to participants 
assigned to CDSMP (n=75) on the primary and secondary outcomes (primary: knowledge and use of 
measures to prevent contracting and spreading COVID-19; secondary: use of healthcare services for 
COVID-related concerns; perceived isolation and social disconnectedness).
Primary Hypothesis: I-IMR participants will demonstrate greater improvements in their scores on 
the primary outcome measure (a measure of knowledge and behaviors to prevent contracting and 
spreading COVID-19 developed for this project) compared to CDSMP participants.
Secondary Hypothesis: I-IMR compared to CDSMP participants will demonstrate less COVID-
related health care service use and greater improvements in social connectedness.   
Exploratory Aim: Explore differential response with respect to race/ethnicity (Black versus White 
adults); age (older versus younger adults); and psychiatric diagnosis (schizophrenia-spectrum vs. mood 
disorders).
Exploratory Hypothesis: Effect sizes for the I-IMR module versus generic support calls alone will be 
even greater among Blacks v. Whites, older (55+) v. younger adults, and people with schizophrenia-
spectrum v. mood disorders.
3. Study Design
The proposed study is to compare I-IMR and CDSMP in improving outcomes of importance to the 
target population of people with SMI: optimizing personal physical and mental health, being an active 
agent in determining personal physical and mental health wellness goals, decreasing risk of early 
mortality, and minimizing use of the ER and hospitals. A secondary objective is to learn whether 
psychiatric diagnosis and/or age interacts with treatment, specifically, whether treatment effects vary 
more for participants with schizophrenia or older age. Consistent with a truly pragmatic trial, inclusion 
criteria are broad and exclusion criteria are limited. Participants will be randomized, stratified by 
diagnosis (schizophrenia spectrum vs. mood disorder) and age group (age 18-49 vs. age 50+). 
Intervention fidelity will be measured but not controlled; likewise, participant engagement will be 
measured but not required. The interventions will be delivered by community providers as part of 
routine services at Centerstone TN and Seven Counties Services in Kentucky to maximize 
generalizability of findings. Mixed quantitative and qualitative methods will be used to assess 
outcomes. Trained raters will administer in-person assessments at baseline, 4, 8, and 12 months. A sub-
sample of participants will be invited to participate in a qualitative interview to describe their treatment 
experience.  Temporarily, during the COVID-19 pandemic, the assessments will be conducted by 
telephone instead of in person.  Participants due for assessments will receive a packet of the response 
options that are normally presented to them during the assessments and instructions for phone-based 
interviews.  Research staff will mail them a gift card (instead of cash) when they complete their 
assessments.

V 03 8 2022
5
Study participants will be 600 adults age 18+ with SMI (diagnosis of schizophrenia, schizoaffective 
disorder, bipolar disorder, chronic depression with moderate impairment in functioning, PTSD, and/or 
other serious anxiety disorders (e.g., Generalized Anxiety Disorder, OCD, Panic Disorder, 
Agoraphobia, etc.) receiving services at Centerstone (TN) and Seven Counties Services (KY) who 
reflect the racial, cultural, linguistic, and ethnic diversity of people with SMI in this agency. The 
race/ethnicity of consumers is as follows: White: 61%, Black: 30%, Hispanic: 3%, Other: 6%; 95% 
English speaking; and 52% Female. Eligibility for the study also includes: diagnosis of a chronic 
medical condition increasing risk of early mortality from cardiovascular or respiratory disease or 
diabetes, hyperlipidemia, hypertension, COPD [including emphysema], asthma, coronary artery 
disease, heart failure, tobacco dependence, obesity), chronic pain, and at least 1 ER visit or 
hospitalization within the past year (and/or 3 or more unscheduled outpatient visits) or judgment by the 
treatment team of substantial need for illness self-management training. Consumers who do not speak 
English will be excluded for logistical reasons. Consumers with either no, or a well-controlled medical 
condition will not be included. Individuals residing in a nursing home or other institution will be 
excluded given the focus on independent management of illness. Because I-IMR and CDSMP involve 
learning new skills, individuals with a chart diagnosis of dementia, or evidence of significant cognitive 
impairment as indicated by the Saint Louis University Mental Status Examination73 score <20, will be 
excluded. Temporarily, during the COVID-19 pandemic, we will use the Telephone Interview for 
Cognitive Status (TICS)120 because the SLUMS cannot be administered over the phone. 
In addition we will conduct an implementation assessment using the Consolidated Framework for 
Implementation Research (CFIR) to specify types of determinants that influence implementation 
outcomes of program acceptance, fidelity, adherence, and sustainability. The CFIR is composed of five 
major domains: Intervention Characteristics, Outer Setting, Inner Setting, Characteristics of 
Individuals, and Process.116 Our research team will conduct semi-structured interviews with 
stakeholders critical to the implementation of the CDSMP and I-IMR interventions in order to 
understand factors influencing implementation. These interviews will be either by phone or in-person 
at baseline, close to the beginning of the implementation of the study interventions. The baseline 
assessment in KY occurred in November 2019.  The baseline assessment in TN was scheduled to occur 
in late January or early February, 2020, but IRB delays have prevented scheduling until April or May 
2020. Subsequent assessments will be at six month intervals.
Since a highly novel aspect of our CER proposal is the comparison of two evidence-based 
interventions delivered by either peers (i.e., CDSMP) or providers (I-IMR), our assessment will 
explore differences in implementation that may be influenced by these intervention characteristics. For 
example, we will inquire about interventionists’ knowledge and beliefs about the intervention, belief in 
their ability to deliver the intervention protocol, and the interventionists’ identification within the 
organization.  The CFIR evaluation will also examine the agency’s capacity for and implementation of 
training and ongoing supervision in the implementation of the intervention for peers and providers.  
We will also explore potential differences in the extent to which participants form positive peer 
networks with other participants by intervention type (peer delivered vs. professional delivered 
programs).  Dr. Kelly Aschbrenner, an experienced implementation scientist and long-time 
collaborator with both study PIs, will lead a mixed methods evaluation of potential determinants of 
implementation outcomes with data collected through telephone interviews and online survey 
assessments with program staff and agency leadership every 6 months while the interventions are 
being delivered as part of the study.  The CFIR-based analysis will examine the relationship between 
factors in a number of domains and implementation outcomes. She will be assisted by a half-time 
research assistant, who will help with administration of surveys and will take copious notes critical to 

V 03 8 2022
6
the evaluation and coding of information provided in semi-structured interviews.
The Needs Assessment required as part of the COVID-related component of the project involves 
qualitative interviews by research staff with 10 clinical staff who work directly with clients, and 20 
participants who have already enrolled in I’M Well. The Site Coordinators and Site PIs at each site will 
meet to select staff who work most closely with service users and therefore are in the best position to 
comment on how they are handling the COVID-19 pandemic and what they need at this time. The Site 
Coordinators will identify participants for the needs assessment, which will be conducted by members 
of the PI’s research team (including review of an information sheet describing the interview and 
process), and will coordinate the appointments and arrange for them to be audiotaped to permit 
evaluation of themes.  We could have used an approach in which each person would have an equal 
chance to participate using randomly generated numbers.  However, this could have resulted in an 
imbalanced group (for example, mostly white males).  We felt it was essential to hear a wide range of 
perspectives, from participants who reflect the characteristics of the broader study population.  For 
example, we wanted to be sure to include equal numbers of males and females, equal numbers of 
CDSMP and I-IMR participants, participants from a diverse set of racial and ethnic backgrounds, older 
and younger people. Therefore, the Study Coordinators will work to ensure that the individuals who 
will be invited are a diverse group. Site Coordinators will also provide the compensation for the 
interviews ($50 VISA card for participants, $25 restaurant gift card for staff).  
These interviews will inform development of the curriculum for the 3-session I-IMR module on 
COVID-19 and other viral diseases.  This component of the project will also involve development of 
the primary outcome measure, given that no appropriate measure of COVID-19 knowledge and use of 
behaviors to avoid catching and spreading disease was available.  After finalizing the curriculum for 
the 3-session I-IMR module, interventionists will be trained to deliver it by phone or video. We will 
record the sessions to permit supervision and fidelity assessment by the co-developers of I-IMR.  All 
participants have already consented to audiotaping or videotaping of IIMR sessions.  Research staff 
will conduct baseline, 6- and 12-week assessments by phone with all participants (75 I-IMR and 75 
CDSMP participants).  (Site Coordinators will first complete informed consent to participate in this 
project, by phone using the procedure described below). We will work with the statisticians from the 
I’M Well project to complete data analyses. 
Site Coordinators will contact individuals who have been randomized to I-IMR and CDSMP to invite 
them to participate in this component of the project.  I-IMR group participants will be offered the 3 
session I-IMR module and asked to participate in a phone-based assessment no more than one week 
prior to receiving the intervention (baseline), a second assessment no more than two weeks after 
completing the 3 sessions (6 week assessment),  and a third assessment approximately 12 weeks after 
receiving the last session of the module (12 week assessment).  CDSMP group participants are 
receiving usual care, which includes generic supportive telephone calls from agency staff.  They will 
be asked to participate in phone-based assessments on the same schedule as the I-IMR participants. For 
both groups (I-IMR & CDSMP), Site Coordinators will call participants to review a description of the 
project in the consent form.  Consent to participate will be obtained by phone. 
Interventions to be Compared
IIMR (Integrated Illness Management and Recovery vs. CDSMP (Stanford Chronic Disease Self- 
Management Program)
Overview of Integrated Illness Management and Recovery (I-IMR): I-IMR combines 4 evidence-
based psychosocial interventions for SMI: (1) psychoeducation, which improves knowledge about 

V 03 8 2022
7
mental illness management,43-45 (2) behavioral tailoring, which improves medication adherence,46-49 
(3) relapse prevention training, which decreases relapses and rehospitalizations,50-55 and (4) coping 
skills training, which reduces distress related to symptoms.56-61 Integrated Illness Management and 
Recovery (I-IMR) was developed by integrating chronic medical illness self-management with 
psychiatric illness self-management.9,11 Currently, I-IMR is available in mental health agencies in New 
York, Massachusetts, New Hampshire, Minnesota, Maryland, Illinois, California, and Indiana. 
The I-IMR curriculum topics are shown in Table 1 below. 
Table 1: Integrated Illness Self-Management and Recovery Curriculum 
Module Topics
Description of Content 
Module 1: Recovery Strategies
Developing personal recovery goals and strategies
Module 2: The Brain-Body Connection 
Understanding the relationship between physical and mental health
Module 3: Practical Facts About Mental and Physical Health 
Conditions 
Fact sheets with education about the cause and course of illnesses
Module 4: Healthy Lifestyles
The impact of exercise, eating, sleep, and tobacco smoking on mental and physical health
Module 5: Using Medications Effectively 
Education on medical and psychiatric medications (side effects, adherence strategies, etc.) 
Module 6: Social Support for Illness Self-Management 
Engaging significant others for medical and psychiatric illness self-management support
Module 7: Managing Stress
Learning strategies to manage stress to reduce its impact on medical and mental health 
Module 8: Managing Persistent Physical and Mental Health 
Symptoms
The interaction of persistent physical and mental health symptoms and how to cope 
Module 9: Relapse Prevention Planning
Identifying early warning signs of physical and mental illness and making an action plan
Module 10: Getting Your Needs Met in the Health Care System
Supplemental Module:  Alcohol and Substance Use
Finding and communicating with health care providers, responding to health emergencies
The impact of alcohol and illicit substances on medical and psychiatric illness management
Overview of the Stanford Chronic Disease Self-Management Program (CDSMP): CDSMP is a 6-
session group-based educational program co-delivered by two peers (lay people who have successfully 
managed a chronic illness) or a peer and a medical professional.21,22 This 6-week program covers 
techniques for dealing with problems such as: frustration, fatigue, pain and isolation; appropriate 
exercise for maintaining and improving strength, flexibility and endurance; appropriate use of 
medications; communicating effectively with family, friends and health professionals; nutrition; and 
evaluating new treatments (see Table 2). A meta-analysis of 23 studies of the CDSMP program in 
general populations conducted by the Centers for Disease Control and Prevention in 2013 found 4-6 
month improvements in energy, fatigue, self-rated health, aerobic exercise, cognitive symptom 
management, psychological health, communication with physicians, and health care utilization.62 
CDSMP is currently offered in over 45 states and over 19 countries.
Table 2: The Stanford Chronic Disease Self-Management (CDSMP) Curriculum
4. Analysis
Evaluation of Group Equivalence. 
Subgroup analysis for treatment effects (i.e., estimate of the treatment effect for each subgroup) is a 
common approach for HTE analysis. However, this approach is susceptible to the issue of multiple 
post hoc analyses. We have a total of 20 subgroup analyses (5 outcome measures by 4 subgroups), thus 
we will adjust the p-value to account for multiple tests for each subgroup analysis and use the false 
discovery rate method90 implemented in SAS software.91 Subgroup analysis may increase the 
-Identifying Common Problems  
-Differences between Acute and 
  Chronic Illnesses 
-Using Your Mind to Manage Symptoms 
-Making an Action Plan 
-Dealing with Difficult Emotions
-Intro to Physical Activity and Exercise 
-Problem Solving 
-Better Breathing 
-Pain and Fatigue Management 
-Endurance Activities 
-Future Plans for Health Care 
-Healthy Eating Communications Skills 
-Medication Usage 
-Making Informed Treatment Decisions 
-Depression Management 
-Positive Thinking 
-Guided Imagery
-Working with Your Health Care Professional 
-Looking Back and Planning for the Future

V 03 8 2022
8
likelihood of Type II error due to small sample sizes. Therefore, we will not rely on statistical tests and 
p-values alone to evaluate HTE; we will rely on estimated effect sizes (estimate divided by the 
standard deviation) and compute confidence intervals around these effect sizes.
Evaluation of Attrition 
We will recruit 600 participants and randomly assign them to two arms with equal allocation (300 per 
arm). Based on previous experience, we assumed 20% attrition by 12 months. In addition to enhancing 
the process of full informed consent, these procedures are designed to decrease the likelihood of study 
attrition due to inadequate appreciation of the time, effort, and component activities involved in 
participating in the intervention program, or in the study protocol.
Analyses for Primary Study Aims and Hypotheses
Analysis for Aim 1:  All analysis will be conducted on the full sample regardless of exposure to 
treatment (intent to treat). Our primary hypothesis in Aim 1 is that I-IMR compared to CDSMP will 
be associated with greater improvements in physical health self-management abilities (Self-Rated 
Abilities for Health Practices) and physical health consumer activation (Patient Activation Measure). 
To test this hypothesis, we will fit generalized linear mixed-effects linear models (GLMM) with 
identity link functions and normal distribution specification. Treatment arm (I-IMR and vs. CDSMP), 
time (baseline, 4-, 8- and 12-month), and arm-by-time interactions will be specified as fixed effects. 
Intercept and time will be specified as random effects to take the correlated nature of the data resulting 
from repeated measures into account. The primary hypothesis of interest will be examined by testing if 
the arm-by-time interaction effect (difference in rate of improvement over 12 month) is significantly 
different from zero. Our secondary hypothesis is that I-IMR compared to CDSMP will have greater 
improvement in self-reported physical health functioning (PROMIS Global Health Measure). The 
same GLMM model will be used to test this hypothesis. Qualitative data for Aims 1 and 2 will be 
audio-recorded, de-identified and transcribed, and analyzed with ATLAS.ti using the “grounded 
theory” approach.85 The Research Support Team will read data and develop operational definitions 
from transcript coding, a validated approach which allows for multiple perspectives.85 Codes will be 
assigned to text, grouped, and checked for themes. The codebook will consist of a priori researcher-
driven codes, derived from interviews and inductively derived codes from qualitative data.85 Analyses 
will assess within group consensus or disagreement. Member checking will be employed to validate 
qualitative results and resolve any incongruent findings.  
               
Analysis for Aim 2:  Our primary hypothesis for Aim 2 is that I-IMR compared to CDSMP will be 
associated with greater improvements in mental health self-management abilities (Illness 
Management and Recovery Scale80); and mental health consumer activation (Patient-Activation 
Measure-MH81); and our secondary hypothesis is that I-IMR compared to CDSMP will be associated 
with greater improvements in self-reported mental health functioning (PROMIS Global Health 
Measure79). We will use the same GLMM approach to test primary and secondary hypotheses for Aim 
2.
Analysis for Aim 3: We hypothesized that (1) participation in I-IMR compared to CDSMP will result 
in greater reductions in emergency room (ER) visits and acute care hospital days compared to the 12 
months before participation; and (2) greater reduction in 10-year cardiovascular mortality risk 
(ACC/AHA Cardiovascular Risk Score82) over 12 months. To test Aim 3 (1), we will compute the total 
number of ER visits and hospital days 12-months before intervention, and 12-months after 
intervention, and fit the same generalized linear mixed model (GLMM) but with log link function and 
Poisson, or negative binomial or zero-inflated distribution (depending on model fit) because the 

V 03 8 2022
9
outcomes are counts and are likely to be skewed with a cluster of zero values.86 The arm, time (before 
vs. after) and arm*time interaction will be included as fixed effects, and the superiority hypothesis of I-
IMR compared to CDSMP will be tested with the significance of the interaction term. For this analysis, 
because we cannot ensure covariates are balanced between two arms 12-months before randomization, 
we will test group (arm) balance with respect to demographic and clinical covariates. Depending on the 
number of imbalances we find, we will either include covariates in the model or use propensity score 
adjustment with inverse probability weighting to balance the groups.
The same GLMM model will be used to test Aim 3 (2), and the same fixed effects, intervention arm, 
time and arm*time interaction will be included in the model, but the time variable has four assessment 
points (baseline, 4-, 8- and 12-month), not two points. Thus, the outcome, 10-year cardiovascular 
mortality risk (ACC/AHA Cardiovascular Risk Score82) is a proportion/percent, which is a continuous 
variable with limited range. The identity link function and normal distribution will be specified for this 
outcome. However, if outcome data does not include the lower and upper limits (0 and 1), the logit link 
function and beta distribution87 will be specified because beta regression is more appropriate for 
percent and proportion outcomes within the 0-1 range.
Analysis for Aim 4: In Aim 4, we compare I-IMR to CDSMP with respect to subgroup differences in 
self-management outcomes by mental illness subgroup (schizophrenia-spectrum vs. mood disorders) 
and by age (18-49 vs. 50+) based on our belief that greater cognitive limitations and comorbid health 
conditions may require more intensive and specialized self-management training consistent with I-
IMR.  We hypothesize that when comparing I-IMR and CDSMP, the difference in treatment effects 
will be greater for schizophrenia spectrum disorders compared to mood disorders; and greater for older 
(age 50+) compared to younger (age 18-49) participants with respect to: physical health self-
management abilities and activation; mental health self-management abilities and activation and 
10-year cardiovascular mortality risk reduction.  In this aim, we examine heterogeneity of treatment 
effects (HTE) using subgroup analyses. Because we pre-specified the 2 subgroups (diagnosis and age) 
and hypothesized the differential treatment effect between subgroups based on the literature, our 
approach is confirmatory (hypothesis driven) HTE analysis. To test HTE, we will use the same 
analytic models, GLMM for Aim 1, Aim 2 and Aim 3, but we will include 3-way interaction terms 
(arm*time*group) in each model. A significant 3-way interaction effect will indicate the existence of 
the heterogeneity of treatment effects between subgroups. Following this step, we will conduct simple 
effect analysis to estimate treatment effect differences (a difference in slopes by time effect between 
arms) within each subgroup. Although our HTE analysis is hypothesis driven, we will report p-values 
for hypothesis testing for all subgroup analyses. We will also evaluate HTE by examining the 
magnitude of treatment effects with standard errors (clinically meaningful effect sizes) as outlined in 
PCORI Methodology Standards88 and recommended by statisticians.89
COVID Component Analyses: The primary outcome is a measure we will create, based in part on a 
large (n=6000) online survey of knowledge and use of measures to prevent contracting and spreading 
COVID-19 that was conducted in April in both the US and the UK. This will be measured at baseline, 
6- and 12-weeks. Covariance pattern modelling will be used for the analysis, with group (intervention 
vs. comparison), time (baseline, 6- and 12 weeks) and group*time interaction included in the model, 
and variance-covariance parameters fully estimated to account for within-subject correlation over time. 
A significant group*time interaction will indicate superiority of I-IMR over CDSMP. In addition to the 
measures used to evaluate the primary and secondary aims, we will also administer the PHQ-9,121 the 
GAD-7,122 and the Fear of COVID Scale123 to assess depression, anxiety, and fear of COVID as 
potential co-variates in the analyses.

V 03 8 2022
10
Power analysis: The primary outcome is continuous, thus power analysis will be based on a linear 
mixed model.  With n=150 (75 per arm), 3 assessments, assuming 10% attrition, within-subject 
correlation of 0.05, 2-tailed test with alpha = 0.05, the study has 80% power to detect an effect size of 
0.46, between small (0.2) and medium (0.5). 
Quantitative Assessment Measures
Table 1. Study Measures
Aim 1: Physical Illness Self- Management and 
Activation
Instrument
Baselin
e
8 wks 4 mo. 8 mo. 12 mo. 
Medical Illness Self- Management
Self-Rated Abilities for Health Practices77 



Consumer Activation
Patient Activation Measure (PAM)78




Self-Reported Physical Health Functioning                 
PROMIS Global Health measure79




Social Functioning Scale
Social Functioning Scale114 




Aim 2: Mental Health Self-Management and Activation
Psychiatric Illness Self- Management
Illness Management and Recovery Scale80 



Consumer Activation
Patient Activation Measure - Mental 
Health81




Self-Reported Mental Health Functioning
PROMIS Global Health measure79




Aim 3: Service Use and Mortality Risk
Mortality Risk
ACC/AHA Cardiovascular Risk Score82




Service Use
Number of Hospital Days and ER visits 
and Cornell Service Index




Carbon Monoxide Level
CO Analyzer PICO+115




Satisfaction Survey

Table 2. COVID-Related Component Assessment Schedule
Aim 1: 
Instrument
Baseline
6 wk
12 
wk
Knowledge and use of measures to prevent 
catching and spreading virus
Measure to be developed as part of 
project



Aim 2:
Use of healthcare for COVID-related issues
Self-reported service use and 
record review



Social determinants of health
UCLA Loneliness Scale124



Lubben Social Network Scale-6125



Social Connectedness (items 
developed for this project)




5. Study Progress Monitoring
Upon completion of trainings, promotion and recruitment will begin at both Seven Counties 
Kentucky and Centerstone Tennessee. Theresa Watson and Ruchita Agrawal, the site PIs in 
Kentucky, will facilitate the research team’s access to the clinical treatment teams at Seven 
Counties Kentucky to identify potentially eligible participants and Dr. Schut will facilitate access at 
Centerstone Tennessee. In addition, the Site PIs will ensure that the research staff have access to 
information needed in the electronic medical record to efficiently screen potential participants. 
Both Site PIs will participate in the weekly project meetings with Dartmouth to discuss pre-
implementation and implementation tasks as well as dissemination of outcomes for the project. Dr. 
Agrawal, Theresa Watson, and Dr. Schut will provide the administrative supervision to the site 
research staff and will periodically attend meetings of the Key Stakeholder Advisory Group and 
Consumer Advisory Group.   

V 03 8 2022
11
In addition to enrolling participants, the research staff hired for this project will be responsible for 
all data collection required for this project.  The Research Coordinators and Research Interviewers 
will conduct baseline assessments with participants; while only the blinded Research Interviewer 
will conduct research assessments at 4, 8, and 12 months follow-up. The Research Coordinators 
will conduct the 8-week phone call to assess satisfaction with the study interventions. The 
Research Coordinators will manage an account to provide reimbursement to participants for 
completing research interviews.  They will also assist in coordination of the Consumer Advisory 
Group meetings and the Key Stakeholder Advisory Group quarterly meetings. 
A schedule of weekly calls will be scheduled with the Dartmouth team for supervision of the 
research staff and for fidelity assurance of the I-IMR and CDSMP models. The Research 
Coordinators and the Research Interviewers will participate in these weekly meetings and the 
intervention facilitators will participate in weekly fidelity assurance calls.  Intervention facilitators 
will audio or video record (mode will depend on whether the sessions are being delivered remotely 
or in-person) CDSMP and I-IMR sessions and submit recordings to the Dartmouth team for review 
and feedback each week. 
Joy Varney, Director of Peer Support Services at Centerstone Kentucky, will be an integral member of 
the project team. She will participate in the weekly project management meetings with the Dartmouth 
team and will also facilitate monthly phone meetings of the Consumer Advisory group with Dr. 
Fortuna.  Ms. Varney and the other 2 Peer Advisors from Kentucky will travel annually to Tennessee 
with Karen Fortuna, PhD for an in-person meeting with the 2 Peer Advisors from Centerstone 
Tennessee.  
6. Risks & Benefits
Note: Risks may be physical, psychological, social, legal, economic, to reputation, or others. 
a. Describe any potential risks, their likelihood and seriousness:
The risks associated with participation in the study are deemed to be minimal and are outweighed by 
the potential benefits. Participation in an illness self-management group, like CDSMP or I-IMR, is no 
greater than participating in similar skills training groups as part of usual mental health care. Anxiety, 
boredom, or related discomfort could occur during self-management sessions or during research 
interviews.  There is also a risk of loss of confidentiality from participating in a group, participating in 
research assessments, and using the mobile app (associated with I-IMR).
b. Confirm that risks to subjects have been minimized, by use of procedures which are 
consistent with sound research design and which do not unnecessarily expose subjects 
to risk:
Anxiety or discomfort during illness self-management groups and during interviews will be handled by 
trained facilitators/interviews who will work individually with participants to develop and enact 
strategies to cope and reduce distress during the groups. Breaks will be offered during both groups and 
interviews as needed as well.  Risks regarding breach of confidentiality with respect to the study data 
will be minimized in several ways. First, we will use using code numbers instead of names on the data.  

V 03 8 2022
12
The data collected using codes will be electronically captured on REDCap, a secure data collection 
program.  The code will be unavailable to anyone outside of the research team, and it will be destroyed 
at the conclusion of the project. With respect to the data that is collected by the smartphone 
application, data are transmitted to ZCO (software development company for the smartphone 
application) server via secure, HIPAA-compliant security protocol over secure cellular connection. 
The data transmitted during sessions do not include participant identifiers. 
c.
Describe why all the risks to subjects are reasonable in relation to both anticipated 
benefits and the knowledge expected to be gained from the study:
The risk to participants is minimal and outweighed by the potential benefit of improving self-
management of illness prompting to engage in illness management and positive coping behaviors,
7. Unexpected Events or Incidental Findings
Describe potential events and provide a plan of action:
Serious, unexpected adverse events (SAE) related to study participation are rare in our research 
groups’ studies. If an SAE occurs, the Co-PIs will report the event to the Dartmouth CPHS and to the 
DSMB. The Co-PIs, in consultation with Co-Is and others as needed, will review the adverse event 
report and gather other information as needed to investigate the event and determine the need for 
subsequent action. Any subsequent action will be documented and reported to the Dartmouth CPHS.
8. Deception
Does any part of this study involve deception or withholding of information from 
participants?
☐  Yes
☒  No
If Yes, provide an explanation which addresses the following:

A description of the deception being used

Why the deception is necessary

A plan for debriefing, or providing subjects with the pertinent information after participation
9. Equitable Participant Selection
a. Estimated number of participants at Dartmouth CPHS reviewed sites: 
600 Adults 18+ and 150 of consented participants will also participate in the COVID-19 
enhancement. 
b. Provide a justification of the proposed sample size

V 03 8 2022
13
The sample size is based on maintaining an adequate sample for data analyses with 
consideration to retention based on previous studies.  Retention in our previous studies and 
in our IIMR studies has been very high. We conservatively estimate a loss of 20% over the 
12-month study, resulting in full follow-up data on n = 480.
c.
Define the target population:
The target population are individuals diagnosed with a SMI (diagnosis of schizophrenia, 
schizoaffective disorder, bipolar disorder, chronic depression with moderate impairment in 
functioning, PTSD, or other serious anxiety disorders) with co-morbid chronic medical problems 
receiving services at Seven Counties Services (KY) and Centerstone (TN) who reflect the racial, 
cultural, linguistic, and ethnic diversity of people with SMI in this agency. 
Inclusion and Exclusion Criteria:  Eligibility for the study also includes: diagnosis of a chronic 
medical condition increasing risk of early mortality from cardiovascular or respiratory disease or 
diabetes, hyperlipidemia, hypertension, COPD [including emphysema], asthma, coronary artery 
disease, heart failure, tobacco dependence, obesity), chronic pain, and at least 1 ER visit or 
hospitalization within the past year (and/or 3 or more unscheduled outpatient visits) or judgment by the 
treatment team of substantial need for illness self-management training. Consumers who do not speak 
English will be excluded for logistical reasons. Consumers with either no, or a well-controlled medical 
condition will not be included. Individuals residing in a nursing home or other institution will be 
excluded given the focus on independent management of illness. Because I-IMR and CDSMP involve 
learning new skills, individuals with a chart diagnosis of dementia, or evidence of significant cognitive 
impairment as indicated by the Saint Louis University Mental Status Examination73 score <20, will be 
excluded.  Temporarily during the COVID-19 pandemic, participants who score <20 on the TICS will 
be excluded.
d. Vulnerable populations
Note: Certain populations are considered vulnerable to coercion and undue influence and 
are provided with additional protections when participating in a research study.  
Identify any of the below populations which you plan to recruit for this study.  In 
addition, complete the form(s) linked with each population as necessary and upload on 
the ‘Supporting Documents’ page in Rapport. 
☒ Pregnant Women, Fetuses and Neonates
☐ Children
☒ People with impaired decision-making capacity
The following populations may also be considered vulnerable to coercion or other 
undue influence:

Prisoners

People who are economically disadvantaged 

The elderly 

People who are illiterate or do not speak English

Students and employees

V 03 8 2022
14
Describe any other potentially vulnerable population(s) and the additional protections 
provided to them:
10. Recruitment
Describe method(s) of recruitment.  Associated advertisements and other materials to be used 
for recruitment should be uploaded to the ‘Consent Forms and Recruitment Materials’ page 
in Rapport.
We will use several approaches that have had proven effectiveness in prior studies with similar 
populations to ensure that all potentially eligible consumers are aware of the opportunity to participate 
in the project and that enrolled participants are retained. Potentially eligible consumers will be 
identified initially through a review by the site research staff of the electronic health record (EHR) and 
elicitation of referrals from the mental health treatment teams.  Posters and pamphlets describing the 
project will also be distributed in the waiting rooms at the study sites. Letters will be sent to potentially 
eligible participants to ensure that all consumers who may qualify are aware of the study. Potential 
participants will be invited to group events to hear descriptions of the study. Email blasts and social 
media venues will be used to stimulate self- and clinical referrals to the study, and a description of the 
study will be included on the websites at the study sites. Following referral, the research staff will 
review eligibility criteria. Potentially eligible participants will be invited to meet with one of the 
research staff for an in-person verification of eligibility and an introductory description of the project 
and study requirements, including description of the study interventions, review of the study protocols, 
randomization, and required research assessments.  Research introduction meetings have been 
successfully used by our research group to assure that study participants fully understand clinical and 
research procedures. In addition to enhancing the process of full informed consent, these procedures 
are designed to decrease the likelihood of study attrition due to inadequate appreciation of the time, 
effort, and component activities involved in participating in the intervention program, or in the study 
protocol.
11. Informed Consent, Assent, and Authorization
Interested individuals who have had an introduction meeting will then meet with a member of the 
research staff to review the informed consent form. Research staff will read the consent form word-for-
word, stopping to check understanding after each paragraph. Competence to provide informed consent 
will be confirmed with a review of personal history and functioning and completion of an informed 
consent evaluation tool that will be designed for the study. If all goes well and the individual has a full 
understanding of the form, they will be asked to sign the consent agreeing to participate. Individuals 
with guardians will be able to participate as long as they are able to understand what the study 
involves. In these instances, assent will be obtained from the client and consent will be obtained from 
the legally designated guardian. Participants (and guardians) will be provided a signed copy of the 
consent form with contact telephone numbers if they have additional questions. 
During the COVID-19 pandemic, we plan to consent new participants remotely.  The staff providing 
services at Centerstone TN and Seven Counties have continued to make referrals of potentially eligible 
service users and the research staff have continued to chart screen potential participants for eligibility. 

V 03 8 2022
15
Following confirmation of eligibility, research staff will call potential participants to describe the 
details of the program and the research in the same way that is was presented to potential participants 
in person before the pandemic.  Staff will mail a copy of the consent form to eligible and interested 
participants and schedule a time to review it, word-for-word, in a phone meeting, exactly as it would 
be reviewed in person, including administration of the consent evaluation. The research staff will 
obtain verbal consent, documenting this on REDCap, and requesting return of a signed copy of the 
consent form in a self-addressed, stamped envelope that will be provided to the participant. 
a. Waiver(s) or alteration(s) may be requested for research that involves no more than 
minimal risk.  
Indicate requested waiver(s) or alteration(s) below.  In addition, complete the 
corresponding section of the Waivers and Alterations Request Form and upload it to 
the ‘Consent Forms and Recruitment Materials’ page in Rapport.     
☐  For the informed consent process
☐ For the documentation of informed consent
☐  For the HIPAA Authorization to use and/or disclose PHI
☐ For a waiver of the requirement for medical record documentation
12. Compensation or Gifts
Please describe any payments, gifts or reimbursements participants will receive for taking 
part in the study:
All consented participants who are eligible will be paid $50 each after completing the baseline and the 
4 and 8 month study visits. Participants will receive $25 for completing a satisfaction survey at 8 
weeks and $75 for completing an assessment at 12 months, for a total of $250 per participant. 
Participants will not be taxed on any money received from this study since it is under the $600 dollars 
per year minimum. 
Individuals participating in the COVID-related needs assessment will receive $50 for a one-hour 
qualitative interview. Research staff at Centerstone and Seven Counties will send a $50 gift card by 
certified mail to the needs assessment participants.  They will also send gift cards for completed 
research assessments for the COVID-related component ($25 baseline,  $50 for 6 weeks, and $75 for 
the 12 week) by certified mail.
13. Privacy of Participants 
Note: Methods used to obtain information about participants may have an effect on privacy.  For 
example:

Consent discussions or interviews held in public which concern sensitive subjects or behaviors

Observations of behavior, especially illicit behavior, in quasi-public settings
Describe any activities or interactions which could lead to a breach of privacy and provide a 
plan to protect participant privacy:
The research staff will be trained by Centerstone and Seven Counties Services in privacy standards 
such as the HIPAA privacy rule. Our research team is experienced in training, instrument 

V 03 8 2022
16
construction, editing, and data management at remote sites. We will use the same procedures for 
data acquisition and management used in prior studies, including: (1) a common core of initial and 
refresher training for staff collecting data; (2) continuous team feedback approach at the sites; and 
(3) final data editing and management at our facility in Concord, NH. Regular conference calls, 
including weekly assessment supervision, will also help ensure standardization of research 
procedures across sites, and identify quality control issues early, allowing consensus solutions to be 
reached. Weekly meetings of the research team provide an environment where quality assurance 
issues can be quickly identified.
14. Confidentiality of Data
Note: Any person engaged in research collecting information about illegal conduct may apply for a 
Certificate of Confidentiality from the National Institute of Health.  
a. If disclosed, could any of the data collected be considered sensitive, with the potential 
to damage financial standing, employability, insurability, or reputation?
☐  No
☒  Yes
If Yes, describe the data or information, the rationale for their collection, and whether 
a Certificate of Confidentiality will be obtained:
In order to participate in this study protocol, the participants must be receiving services at 
one of the community mental health centers.  Although participation in mental health 
services and information related to receipt of services, such as mental health diagnosis, may 
be considered “sensitive,” we will not apply for a Certificate of Confidentiality as this 
would suggest that having a mental illness is akin to using illegal substances in that it could 
leave a person vulnerable to undesirable consequences related to employability, financial, 
legal status, etc.  The suggestion that having a mental illness is something that requires 
protection from a Certificate of Confidentiality serves to further stigmatize individuals with 
mental illness, who should not be made to feel ashamed or compelled to hide the fact that 
they have a mental health diagnosis.  
b. Describe the safeguards employed to secure, share, and maintain data during the 
study, addressing any of the following which may apply: 
Risks regarding breach of confidentiality with respect to the study data will be minimized in several 
ways. First, we will use using code numbers instead of names on the data.  The data collected using 
codes will be electronically captured on REDCap, a secure data collection program.  The code will be 
unavailable to anyone outside of the research team, and it will be destroyed at the conclusion of the 
project.  No medical records or protected health information shall be re-disclosed. A Certificate of 
Confidentiality will not be required. Other confidentiality protections include confidentiality training 
for all new staff and refresher seminars annually for all staff; removing or obscuring names from data 
forms; using an acronym in return addresses on correspondence to participants; and password-
protected computer databases. The risks to participants will also be minimized by reporting on all 
findings in an aggregate, anonymous manner.  In addition, data access will be restricted to members of 

V 03 8 2022
17
the evaluation team only and the data will be stored on a password protected computer and kept in a 
locked filing cabinets at the research sites.
c.
Describe the plan for storage or destruction of data upon study completion:
Data will be stored for up to ten years following the end of the study.  Electronic files will 
be stored on password protected computers and backed up by a remote online server that 
encrypts the data and is also password protected.  If the data is no longer being used, we 
will systematically delete all electronic files.  Paper files will be kept in a locked filing 
cabinet in a locked office.  In the future, if data is no longer being used, all files will be 
shredded and discarded. 

V 03 8 2022
18
REFERENCES CITED 
For detailed instructions, refer to the Application Guidelines for this PFA. Do not exceed 10 pages.
Follow scholarly citation practice and list the source material cited in your Research Plan. PCORI suggests 
using American Medical Association citation style, but other citation styles are accepted.
1.
Kessler RC, Chiu WT, Demler O, Merikangas KR, Walters EE. Prevalence, severity, and comorbidity of 
12-month DSM-IV disorders in the National Comorbidity Survey Replication. Arch Gen Psychiatry. 
2005;62(6):617-627.
2.
Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications: 
a systematic review and meta-analysis. JAMA Psychiatry. 2015;72(4):334-341.
3.
Druss BG, Zhao L, Von Esenwein S, Morrato EH, Marcus SC. Understanding excess mortality in persons 
with mental illness: 17-year follow up of a nationally representative US survey. Med Care. 
2011;49(6):599-604.
4.
Colton CW, Manderscheid RW. Congruencies in increased mortality rates, years of potential life lost, 
and causes of death among public mental health clients in eight states. Preventing Chronic Disease: 
Public Health Research, Practice, and Policy. 2006;3(2):1-14.
5.
Mortensen PB, Juel K. Mortality and causes of death in first admitted schizophrenic patients. Br J 
Psychiatry. 1993;163(2):183-189.
6.
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.
7.
SAMHSA. "10x10 Wellness Campaign: Information for General Health Care Providers". 2010; 
http://store.samhsa.gov/shin/content/SMA10-4566/SMA10-4566.pdf Accessed Sept. 27, 2017.
8.
Bartels SJ, Pratt SI, Aschbrenner KA, et al. Clinically significant improved fitness and weight loss among 
overweight persons with serious mental illness. Psychiat Serv. 2013;64(8):729-736.
9.
Mueser KT, Bartels SJ, Santos M, Pratt SI, Riera EG. Integrated illness management and recovery: a 
program for integrating physical and psychiatric illness self-management in older persons with severe 
mental illness. American Journal of Psychiatric Rehabilitation. 2012;15(2):131-156.
10.
Lorig K, Ritter PL, Pifer C, Werner P. Effectiveness of the chronic disease self-management program for 
persons with a serious mental illness: a translation study. Community Ment Health J. 2014;50(1):96-
103.
11.
Bartels SJ, Pratt SI, Mueser KT, et al. Integrated IMR for psychiatric and general medical illness for 
adults aged 50 or older with serious mental illness. Psychiat Serv. 2014;65(3):330-337.
12.
Bartels SJ, Pratt SI, Aschbrenner KA, et al. Pragmatic replication trial of health promotion coaching for 
obesity in serious mental illness and maintenance of outcomes. Am J Psychiatry. 2015;172(4):344-352.
13.
Aschbrenner KA, Naslund JA, Shevenell M, Kinney E, Bartels SJ. A Pilot Study of a Peer-Group Lifestyle 
Intervention Enhanced With mHealth Technology and Social Media for Adults With Serious Mental 
Illness. J Nerv Ment Dis. 2016;204(6):483-486.
14.
Brunette MF, Ferron JC, McHugo GJ, et al. An electronic decision support system to motivate people 
with severe mental illnesses to quit smoking. Psychiat Serv. 2011;62(4):360-366.
15.
Aschbrenner KA, Naslund JA, Gill L, Bartels SJ, O’Malley AJ, Brunette MF. Preferences for smoking 
cessation support from family and friends among adults with serious mental illness. The Psychiatric 
quarterly. 2017:1-10.
16.
Druss BG, von Esenwein SA, Glick GE, et al. Randomized trial of an integrated behavioral health home: 
the Health Outcomes Management and Evaluation (HOME) study. Am J Psychiatry. 2017;174(3):246-
255.
17.
Andrews AO, Bartels SJ, Xie H, Peacock WJ. Increased risk of nursing home admission among middle 
aged and older adults with schizophrenia. The American journal of geriatric psychiatry : official journal 
of the American Association for Geriatric Psychiatry. 2009;17(8):697-705.
18.
Scharf DM, Schmidt Hackbarth N, Eberhart NK, et al. General medical outcomes from the Primary and 
Behavioral Health Care Integration grant program. Psychiat Serv. 2016;67(11):1226-1232.

V 03 8 2022
19
19.
Saha S, Chant D, McGrath J. A systematic review of mortality in schizophrenia: is the differential 
mortality gap worsening over time? Arch Gen Psychiatry. 2007;64(10):1123-1131.
20.
Scharf DM, Eberhart NK, Hackbarth NS, et al. Evaluation of the SAMHSA Primary and Behavioral Health 
Care Integration (PBHCI) grant program: final report. RAND Corporation;2013.
21.
Lorig KR, Ritter P, Stewart AL, et al. Chronic disease self-management program: 2-year health status 
and health care utilization outcomes. Med Care. 2001;39(11):1217-1223.
22.
Lorig KR, Sobel DS, Stewart AL, et al. Evidence suggesting that a chronic disease self-management 
program can improve health status while reducing hospitalization - A randomized trial. Medical Care. 
1999;37(1):5-14.
23.
Mitchell AJ, Malone D. Physical health and schizophrenia. Curr Opin Psychiatry. 2006;19(4):432-437.
24.
Goldman LS. Medical illness in patients with schizophrenia. J Clin Psychiatry. 1999;60 Suppl 21(Suppl 
21):10-15.
25.
Beyer J, Kuchibhatla M, Gersing K, Krishnan KR. Medical comorbidity in a bipolar outpatient clinical 
population. Neuropsychopharmacology. 2005;30(2):401-404.
26.
Kilbourne AM, Cornelius JR, Han X, et al. Burden of general medical conditions among individuals with 
bipolar disorder. Bipolar Disord. 2004;6(5):368-373.
27.
Allison DB, Casey DE. Antipsychotic-induced weight gain: a review of the literature. J Clin Psychiatry. 
2001;62 Suppl 7(Suppl 7):22-31.
28.
Baxter DN. The mortality experience of individuals on the Salford Psychiatric Case Register. I. All-cause 
mortality. Br J Psychiatry. 1996;168(6):772-779.
29.
Lambert TJ, Velakoulis D, Pantelis C. Medical comorbidity in schizophrenia. Med J Aust. 2003;178 
Suppl(Suppl):S67-70.
30.
Sokal J, Messias E, Dickerson FB, et al. Comorbidity of medical illnesses among adults with serious 
mental illness who are receiving community psychiatric services. Journal of Nervous and Mental 
Disease. 2004;192(6):421-427.
31.
Bartels SJ, Clark RE, Peacock WJ, Dums AR, Pratt SI. Medicare and medicaid costs for schizophrenia 
patients by age cohort compared with costs for depression, dementia, and medically ill patients. The 
American journal of geriatric psychiatry : official journal of the American Association for Geriatric 
Psychiatry. 2003;11(6):648-657.
32.
Konetzka RT, Karon SL, Potter DE. Users of Medicaid home and community-based services are 
especially vulnerable to costly avoidable hospital admissions. Health Aff (Millwood). 2012;31(6):1167-
1175.
33.
Viron MJ, Stern TA. The impact of serious mental illness on health and healthcare. Psychosomatics. 
2010;51(6):458-465.
34.
Siantz E, Aranda MP. Chronic disease self-management interventions for adults with serious mental 
illness: a systematic review of the literature. Gen Hosp Psychiatry. 2014;36(3):233-244.
35.
Kelly EL, Fenwick KM, Barr N, Cohen H, Brekke JS. A systematic review of self-management health care 
models for individuals with serious mental illnesses. Psychiat Serv. 2014;65(11):1300-1310.
36.
Whiteman KL, Naslund JA, DiNapoli EA, Bruce ML, Bartels SJ. Systematic review of integrated general 
medical and psychiatric self-management interventions for adults with serious mental illness. Psychiat 
Serv. 2016;67(11):1213-1225.
37.
Bartels SJ. Caring for the whole person: integrated health care for older adults with severe mental 
illness and medical comorbidity. J Am Geriatr Soc. 2004;52(12 Suppl):S249-257.
38.
Bartels SJ, Forester B, Mueser KT, et al. Enhanced skills training and health care management for older 
persons with severe mental illness. Community Ment Health J. 2004;40(1):75-90.
39.
Bartels SJ, Pratt SI, Mueser KT, et al. Long-term outcomes of a randomized trial of integrated skills 
training and preventive healthcare for older adults with serious mental illness. The American journal of 
geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry. 
2014;22(11):1251-1261.

V 03 8 2022
20
40.
Morden NE, Mistler LA, Weeks WB, Bartels SJ. Health care for patients with serious mental illness: 
family medicine's role. J Am Board Fam Med. 2009;22(2):187-195.
41.
Mueser KT, Pratt SI, Bartels SJ, et al. Randomized trial of social rehabilitation and integrated health 
care for older people with severe mental illness. J Consult Clin Psychol. 2010;78(4):561-573.
42.
Pratt SI, Bartels SJ, Mueser KT, Forester B. Helping older people experience success: an integrated 
model of psychosocial rehabilitation and health care management for older adults with serious mental 
illness. American Journal of Psychiatric Rehabilitation. 2008;11(1):41-60.
43.
Anderson CM, Reiss DJ, Hogarty GE. Schizophrenia and the family: a practitioner's guide to 
psychoeducation and management. New York, N.Y.: Guilford Press; 1986.
44.
Daley DC, Bowler K, Cahalane H. Approaches to patient and family education with affective disorders. 
Patient education and counseling. 1992;19(2):163-174.
45.
Goldman CR, Quinn FL. Effects of a patient education program in the treatment of schizophrenia. 
Hospital & community psychiatry. 1988;39(3):282-286.
46.
Boczkowski JA, Zeichner A, DeSanto N. Neuroleptic compliance among chronic schizophrenic 
outpatients: an intervention outcome report. J Consult Clin Psychol. 1985;53(5):666-671.
47.
Azrin NH, Teichner G. Evaluation of an instructional program for improving medication compliance for 
chronically mentally ill outpatients. Behaviour research and therapy. 1998;36(9):849-861.
48.
Cramer JA, Rosenheck R. Enhancing medication compliance for people with serious mental illness. J 
Nerv Ment Dis. 1999;187(1):53-55.
49.
Kelly GR, Scott JE, Mamon J. Medication compliance and health education among outpatients with 
chronic mental disorders. Med Care. 1990;28(12):1181-1197.
50.
Buchkremer G, Fiedler P. Kognitive vs. handlungsorientierte therapie (Cognitive vs. action-oriented 
treatment). Nervenarzt. 1987;58:481-488.
51.
Lewandowski L, Buchkremer G, Stark M. Das Gruppenklima und die Therapeut-Patient-Baziehung bei 
zwei Gruppentherapiestrategien für schizophrene Patienten--ein Beitrag zur Klärung differentieller 
Therapieeffeckre. Psychotherapie Psychosomatik Medizinische Psychologie. 1994;44:115-121.
52.
Herz MI, Lamberti JS, Mintz J, et al. A program for relapse prevention in schizophrenia: a controlled 
study. Arch Gen Psychiatry. 2000;57(3):277-283.
53.
Perry A, Tarrier N, Morriss R, McCarthy E, Limb K. Randomised controlled trial of efficacy of teaching 
patients with bipolar disorder to identify early symptoms of relapse and obtain treatment. BMJ. 
1999;318(7177):149-153.
54.
Lam DH, Bright J, Jones S, et al. Cognitive therapy for bipolar illness - A pilot study of relapse 
prevention. Cognitive Therapy and Research. 2000;24(5):503-520.
55.
Scott J, Garland A, Moorhead S. A pilot study of cognitive therapy in bipolar disorders. Psychol Med. 
2001;31(3):459-467.
56.
Leclerc C, Lesage AD, Ricard N, Lecomte T, Cyr M. Assessment of a new rehabilitative coping skills 
module for persons with schizophrenia. Am J Orthopsychiatry. 2000;70(3):380-388.
57.
Lecomte T, Cyr M, Lesage AD, Wilde J, Leclerc C, Ricard N. Efficacy of a self-esteem module in the 
empowerment of individuals with schizophrenia. J Nerv Ment Dis. 1999;187(7):406-413.
58.
Schaub A. Cognitive-behavioural copingorientated therapy for schizophrenia: a new treatment model 
for clinical service and research. In: Perris C, McGorry P, eds. Cognitive Psychotherapy of Psychotic and 
Personality Disorders: Handbook of Theory and Practice. Chichester, England: John Wiley & Sons; 1998.
59.
Schaub A, Mueser K. Coping-oriented treatment of schizophrenia and schizoaffective disorder: 
rationale and preliminary results. Annual convention of the Association for the Advancement of 
Behavior Therapy; Nov. 16–19, 2000; New Orleans.
60.
Tarrier N, Beckett R, Harwood S, et al. A trial of two cognitive-behavioral methods of treating drug-
resistant residual psychotic symptoms in schizophrenic patients: I. outcome. British Journal of 
Psychiatry. 1993;162(4):524-532.

V 03 8 2022
21
61.
Tarrier N, Sharpe L, Beckett R, et al. A trial of two cognitive behavioural methods of treating drug-
resistant residual psychotic symptoms in schizophrenia patients: II. treatment-specific changes in 
coping and problem-solving skills. Psychiatry and Psychiatric Epidemiology. 1993;28:5-10.
62.
Brady TJ, Murphy L, O'Colmain BJ, et al. A meta-analysis of health status, health behaviors, and 
healthcare utilization outcomes of the Chronic Disease Self-Management Program. Preventing chronic 
disease. 2013;10:120112.
63.
Mueser KT, Corrigan PW, Hilton DW, et al. Illness management and recovery: a review of the research. 
Psychiat Serv. 2002;53(10):1272-1284.
64.
Hasson-Ohayon I, Roe D, Kravetz S. A randomized controlled trial of the effectiveness of the illness 
management and recovery program. Psychiat Serv. 2007;58(11):1461-1466.
65.
Levitt AJ, Mueser KT, Degenova J, et al. Randomized controlled trial of illness management and 
recovery in multiple-unit supportive housing. Psychiat Serv. 2009;60(12):1629-1636.
66.
Fardig R, Lewander T, Melin L, Folke F, Fredriksson A. A randomized controlled trial of the illness 
management and recovery program for persons with schizophrenia. Psychiat Serv. 2011;62(6):606-
612.
67.
Mueser KT, Meyer PS, Penn DL, Clancy R, Clancy DM, Salyers MP. The Illness Management and 
Recovery program: rationale, development, and preliminary findings. Schizophr Bull. 2006;32 Suppl 
1(suppl 1):S32-43.
68.
Fortuna KL, DiMilia PR, Lohman MC, et al. Feasibility, acceptability, and preliminary effectiveness of a 
peer-delivered and technology supported self-management intervention for older adults with serious 
mental illness. The Psychiatric quarterly. 2017.
69.
Druss BG, Zhao L, von Esenwein SA, et al. The Health and Recovery Peer (HARP) program: a peer-led 
intervention to improve medical self-management for persons with serious mental illness. Schizophr 
Res. 2010;118(1-3):264-270.
70.
Thorpe KE. Building evidence-based interventions to avert diesease and reduce health care spending. 
2011. Accessed Oct. 17, 2017.
71.
Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the chronic care model in the new millennium. 
Health Aff (Millwood). 2009;28(1):75-85.
72.
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 Rehabilitation Journal. 
2013;36(4):278-288.
73.
Shwartz SK, Morris  RD, Penna S. Psychomentric properties of the Saint Louis Univeristy Mental Status 
Examination. Applies Neuropsychology: Adult Journal. 2017; 26(2):101-110. 
74.
Whiteman KL, Lohman MC, Gill LE, Bruce ML, Bartels SJ. Adapting a psychosocial intervention for 
smartphone delivery to middle-aged and older adults with serious mental illness. American Journal of 
Geriatric Psychiatry. 2017;25(8):819-828.
75.
Whiteman KL, Lohman MC, Bartels SJ. A peer- and technology-supported self-management 
intervention. Psychiatric Services. 2017;68(4):420-420.
76.
Pratt SI, Bartels SJ, Mueser KT, et al. Feasibility and effectiveness of an automated telehealth 
intervention to improve illness self-management in persons with serious psychiatric and medical 
disorders. Psychiatric Rehabilitation Journal. 2013;36(4):297-305.
77.
Becker H, Stuifbergen A, Soo Oh H, Hall S. Self-rated abilities for health practices: a health self-efficacy 
measure. Health Values. 1993;17:42-50.
78.
Hibbard JH, Stockard J, Mahoney ER, Tusler M. Development of the Patient Activation Measure (PAM): 
conceptualizing and measuring activation in patients and consumers. Health Serv Res. 2004;39(4 Pt 
1):1005-1026.
79.
Barile JP, Reeve BB, Smith, AW, Zack, MM, Mitchell, SA, Kobau R, Thompson WW. Monitoring 
population health for healthy people 2020: Evaluation of the NIH PROMIS® Global Health, Cdc Healthy 
Days, and Satisfaction with Life Instruments. Quality of Life Research. 2013;22(6), 1201-
1211.  http://dx.doi.org/10.1007/s11136-012-0246-z.

V 03 8 2022
22
80.
Hasson-Ohayon I, Roe D, Kravetz S. The psychometric properties of the Illness Management and 
Recovery scale: client and clinician versions. Psychiatry Res. 2008;160(2):228-235.
81.
Green CA, Perrin NA, Polen MR, Leo MC, Hibbard JH, Tusler M. Development of the Patient Activation 
Measure for Mental Health (PAM-MH). Administration and Policy in Mental Health. 2010;37(4):327-
333.
82.
Goff DC, Lloyd-Jones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of 
cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task 
Force on Practice Guidelines. J Am Coll Cardiol. 2014;63(25 Pt B):2935-2959.
83.
Boothroyd RA, Chen HJ. The psychometric properties of the Colorado Symptom Index. Adm Policy 
Ment Health. 2008;35(5):370-378.
84.
Katz JN, Chang LC, Sangha O, Fossel AH, Bates DW. Can comorbidity be measured by questionnaire 
rather than medical record review? Med Care. 1996;34(1):73-84.
85.
Martin PY, Turner BA. Grounded theory and organizational research. The Journal of Applied Behavioral 
Science. 1986;22(2):141-157.
86.
Xie H, Tao J, McHugo GJ, Drake RE. Comparing statistical methods for analyzing skewed longitudinal 
count data with many zeros: an example of smoking cessation. Journal of substance abuse treatment. 
2013;45(1):99-108.
87.
Zimprich D. Modeling change in skewed variables using mixed beta regression models. Research in 
Human Development. 2010;7(1):9-26.
88.
PCORI methodology standards. Washington, DC: Patient-Centered Outcomes Research Institute;2012.
89.
Lagakos SW. The challenge of subgroup analyses--reporting without distorting. The New England 
journal of medicine. 2006;354(16):1667-1669.
90.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to 
multiple testing. Journal of the Royal Statistical Society Series B (Methodological). 1995;57(1):289-300.
91.
Westfall PH, Tobias RD, Wolfinger RD. Multiple comparisons and multiple tests using SAS. 2 ed: SAS 
Publishing; 2011.
92.
Hedeker D, Gibbons R. Longitudinal data analysis. New Jersey: Wiley-Interscience; 2006.
93.
Molenberghs G, Kenward MG. Missing data in clinical studies. Online: John Wiley & Sons, Ltd; 2007.
94.
Hedeker D, Gibbons RD. Application of random-effects pattern-mixture models for missing data in 
longitudinal studies. Psychological Methods. 1997;2(1):64-78.
95.
Diggle PJ, Heagerty P, Liang KY, Zeger SL. Analysis of longitudinal data. 2 ed. Oxford: Oxford University 
Press; 2002.
96.
Lu K, Luo X, Chen PY. Sample size estimation for repeated measures analysis in randomized clinical 
trials with missing data. The international journal of biostatistics. 2008;4(1):Article 9.
97.
Donohue MC, Gamst AC, Edland SD. Package 'longpower'. Biometrics. 2013;53(3):937-947.
98.
Seale C. Grounding theory. London: SAGE Publications; 1999.
99.
Creswell JW. Qualitative inquiry and research design: choosing among five traditions. Thousand Oaks, 
CA: SAGE Publications; 1998.
100.
Leon AC, Heo M. Sample sizes required to detect interactions between two binary fixed-effects in a 
mixed-effects linear regression model. Computational statistics & data analysis. 2009;53(3):603-608.
101.
Sirey JA, Meyers BS, Teresi JA, Bruce ML, Ramirez M, Raue PJ, Perlick DA, Holmes D.  The Cornell 
Service Index as a measure of health service use. Psychiatric Services. 2005; 56:1564–1569.
102.
Clark RE, Ricketts SK, McHugo GJ. Measuring hospital use without claims: a comparison of patient and 
provider reports. Health Services Research. 1996;31(2):153-169.
103.
Belin TR, Normand SLT. The role of ANCOVA in analyzing experimental data. Psychiatric Annals. 
2009;39(7):753-760.
104.
Kraemer HC. A source of false findings in published research studies: Adjusting for covariates. JAMA 
psychiatry. 2015;72(10):961-962.
105.
Moher D, Hopewell S, Schulz KF, et al. CONSORT 2010 explanation and elaboration: Updated guidelines 
for reporting parallel group randomised trials. BMJ. 2010;340:c869. doi: 10.1136/bmj.c869 [doi].

V 03 8 2022
23
106.
Killip S, Mahfoud Z, Pearce K. What is an intracluster correlation coefficient? Crucial concepts for 
primary care researchers. The Annals of Family Medicine. 2004;2(3):204-208.
107.
Sedgwick P. Cluster randomised controlled trials: Sample size calculations. BMJ: British Medical Journal 
(Online). 2013;346.
108.
Hibbard JH, Mahoney ER, Stock R, & Tusler M. Do increases in patient activation result in improved 
self-management behaviors?  Health Services Research. 2007; 42:1443-1463.
109.
Skolasky RL, Green AF, Scharfstein D, Boult C, Reider L, & Wegener ST. Psychometric properties of the 
patient activation measure among multimorbid older adults. Health Services Research. 2011; 46(2): 
457-478.
110.
Hayes RD, Bjorner JB, Revick DA, Spritzer KL, & Cella D. Development of physical and mental health 
summary scores from the patient-reported outcomes measurement information system (PROMIS) 
global items.  Quality of Life Research. 2009; 18:873-880.
111.
Green CA, Perrin NA, Polen MR, Leo MC, Hibbard JH, & 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.
112.
Norman GR, Sloan JA, & Wyrwich KW. The truly remarkable universality of half a standard deviation: 
confirmation through another look. Expert Rev. Pharmacoeconomics Outcomes Res. 2004;4(5):581-
585.
113.
Salyers MP, Rollins AL, Clendenning D, McGuire AB, & Kim E.  Impact of Illness Management and 
Recovery programs on hospital and emergency room use by Medicaid enrollees.  Psychiatric Services. 
2011;62: 509-515.
114. 
Birchwood M. Smith J, Cochrane R, Wetton S & Copestake S.  The Social Functioning Scale, the 
development and validation of a new scale of social adjustment for use in family intervention 
programmes with schizophrenic patients.  British Journal of Psychiatry, 157(6), 853-859. 
doi:10.1192/bjp.157.6.853.
115.
https://www.covita.net/wp-content/uploads/Micropro-Manual-Issue-3.pdf.
116. 
Keith, RE, Crosson, JC, O’Malley, AS, Cromp, D, & Taylor, EF. Using the Consolidated Framework for 
Implementation Research (CFIR) to produce actionable findings: a rapid-cycle evaluation approach to 
improving implementation. Implementation Science, 12(1). doi: 10.1186/s13012-017-0550-7.
117.
Walker ER, McGee R, Druss BG: Mortality in mental disorders and global disease burden implications: 
A systematic review and meta-analysis. JAMA Psychiatry 2015;72(4):334-41.
118.
Bartels SJ, DiMilia P: Why serious mental illness should be designated a health disparity and the 
paradox of ethnicity. Lancet Psychiatry 2017; 4(5): 351-352.
119.
Pratt, LA: Characteristics of adults with serious mental illness in the United States household 
population in 2007. Psychiatr Serv 2012;63(10):1042-6.
120.      Brandt, J., Spencer, M., & Folstein, M. (1988). The telephone interview for cognitive 
status. Neuropsychiatry Neuropsychol Behav Neurol, 1(2), 111-117.
121.      Kroenke, K. & Spitzer, R.L. (2002). The PHQ-9: A new depression and diagnostic severity measure. 
Psychiatric Annals, 32, 509-521.
122.       Spitzer, R.L., Kroenke, K., Williams, J.B., & Lowe, B. (2006). A brief measure for assessing generalized 
anxiety disorder: the GAD-7. Archive of Internal Medicine, 166(10), 1098-1097.
123.       Ahorsu, D. K., Lin, C. Y., Imani, V., Saffari, M., Griffiths, M. D., & Pakpour, A. H. (2020). The Fear of 
COVID-19 Scale: Development and Initial Validation. International journal of mental health and 
addiction, 1–9. Advance online publication. https://doi.org/10.1007/s11469-020-00270-8.
124.      Huges, M.E., Waite, L.J., Hawley, L.C., & Cacioppo, J.T. (2004). A short scale for measuring loneliness in 
large surveys: Results from two population-based studies. Research on Aging, 26(6), 655-672.
125.       Lubben, J., Blozik, E., Gilmann, G., Iliffe, S., von Renteln Kruse, W., Beck, J.C., & Stuck, A.E.  (2006). 
Performance of an abbreviated version of the Lubben Social Network Scale among three European 
community-dwelling older adult populations. Gerontologist, 46(4), 503-513.

V 03 8 2022
24