Implementing and Sustaining a Transdiagnostic Sleep and Circadian Treatment to Improve Severe Mental Illness Outcomes in Community Mental Health Part 1: Implementation
Phase: NA
Conditions: Sleep Disorder, Circadian Dysregulation
Sponsor: University of California, Berkeley

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Title: 
Implementing and Sustaining a Sleep Treatment to 
Improve Community Mental Health Part 1: 
Implementation Health Outcomes 
CPHS Number: 
2019-04-12091 (Date of 1st  approval: May 23, 2019) 
Clinicaltrials.gov Number: 
NCT04154631 (Date of 1st  release: November 04, 2019) 
ClinicalTrials.gov Amendment Release Dates 
Amendment 1 Date: 04/14/2020 
Amendment 2 Date: 07/07/2020 
Amendment 3 Date: 09/18/2020 
Amendment 4 Date: 10/06/2020 
Amendment 5 Date: 11/01/2021 
Amendment 6 Date: 08/18/2022 
Amendment 7 Date: 12/30/2022 
Amendment 8 Date: 03/27/2023 
Amendment 9 Date: 02/14/2024 
Amendment 10 Date: 07/02/2024 
Amendment 11 Date: 07/29/2024 
 
 
 
 
Sponsor 
National Institute of Mental Health (Grant number: R01MH120147) 
 
 
Study Principal Investigator 
Allison G. Harvey, Ph.D. Psychology Department, 2121 Berkeley Way, 3rd floor. University of 
California, Berkeley, CA 94720-1650. USA. email: aharvey@berkeley.edu 
 
 

2 
 
 
TABLE OF CONTENTS 
TABLE OF CONTENTS ................................................................................................................ 2 
ABBREVIATIONS AND DEFINITIONS OF TERMS ................................................................ 4 
ABSTRACT .................................................................................................................................... 5 
Table 1. SPIRIT Depiction of Timing and Measures Collected for Implementation Phase........... 6 
Figure 1. Community Mental Health Center (CMHC) Randomization and Patient Timeline ....... 7 
Figure 2. Provider Timeline ............................................................................................................ 8 
1 BACKGROUND ......................................................................................................................... 9 
2 AIMS.......................................................................................................................................... 10 
3 METHODS/DESIGN ................................................................................................................ 11 
3.1 Facilitation ........................................................................................................................... 11 
3.3 Recruitment ......................................................................................................................... 13 
3.3.1 Community Mental Health Centers .............................................................................. 13 
3.3.2 Providers ....................................................................................................................... 13 
3.3.3 Patients.......................................................................................................................... 13 
3.4 Interventions ........................................................................................................................ 14 
3.4.1 Standard TranS-C ......................................................................................................... 14 
3.4.2 Adapted TranS-C .......................................................................................................... 14 
3.4.3 UC-DT .......................................................................................................................... 15 
3.5 Measures.............................................................................................................................. 15 
3.5.1 Providers ....................................................................................................................... 15 
3.5.2 Patients.......................................................................................................................... 16 
3.6 Procedure ............................................................................................................................. 17 
3.6.1 Providers ....................................................................................................................... 17 
3.6.2 Patients.......................................................................................................................... 17 
3.7 Allocation ............................................................................................................................ 18 
3.8 Sample Size ......................................................................................................................... 18 
3.9 Data Management and Dissemination................................................................................. 18 
3.10 Roles and Responsibilities ................................................................................................ 19 
3.11 Changes to Preregistration ................................................................................................ 19 
4 PLANNED ANALYSES ........................................................................................................... 20 
4.1 Preliminary Analyses and Missing Data ............................................................................. 20 
4.2 Dropout................................................................................................................................ 20 

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4.3 Aim 1: Effectiveness Outcomes of Standard or Adapted TranS-C versus UC-DT ............ 20 
4.4 Aim 2: Adapted TranS-C versus Standard TranS-C on Fit to CMHC Context .................. 21 
4.5 Aim 3: Fit as a Mediator of Treatment Condition and Patient Outcome ............................ 21 
4.6 Sensitivity Analyses ............................................................................................................ 21 
4.7 Exploratory Aim 1: TranS-C Treatment Condition on Credibility and PhenX Toolkit...... 22 
4.8 Exploratory Aim 2: Treatment Effects Moderated by Risk Factors ................................... 22 
5 DISCUSSION ............................................................................................................................ 22 
6 TRIAL STATUS ........................................................................................................................ 24 
7 DECLARATIONS ..................................................................................................................... 24 
7.1 Ethics approval and consent to participate .......................................................................... 24 
7.2 Consent for publication ....................................................................................................... 24 
7.3 Availability of data and materials ....................................................................................... 24 
7.4 Competing interests ............................................................................................................. 24 
7.5 Authors' contributions ......................................................................................................... 24 
7.6 Acknowledgements ............................................................................................................. 24 
REFERENCES ............................................................................................................................. 25 
 
 
 

4 
 
 
ABBREVIATIONS AND DEFINITIONS OF TERMS 
 
CMHC 
 
Community mental health center 
EBPT  
 
Evidence-based psychological treatment 
ICC 
 
 
Intra-class correlation 
MLM  
 
Multi-level modeling 
PARiHS 
 
Promoting Action on Research Implementation in Health Services 
PI 
 
 
Principal investigator 
PROMIS-SD  
PROMIS-Sleep Disturbance 
PROMIS-SRI  
PROMIS-Sleep Related Impairment 
REP 
 
 
Replicating Effective Programs 
SMI 
 
 
Serious mental illness 
TranS-C 
 
Transdiagnostic Intervention for Sleep and Circadian Dysfunction;  
UC-DT 
 
Usual care followed by delayed treatment with TranS-C 
 

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ABSTRACT 
 
Background: 
Serious mental illness (SMI) can have devastating consequences. Unfortunately, many patients with SMI 
do not receive evidence-based psychological treatment (EBPTs) in routine practice settings. One barrier is 
poor “fit” between EBPTs and contexts in which they are implemented. The present study will evaluate 
implementation and effectiveness outcomes of the Transdiagnostic Intervention for Sleep and Circadian 
Dysfunction (TranS-C) implemented in community mental health centers (CMHCs). TranS-C was 
designed to target a range of SMI diagnoses by addressing a probable mechanism and predictor of SMI: 
sleep and circadian problems. We will investigate whether adapting TranS-C to fit CMHC contexts 
improves providers’ perceptions of fit and patient outcomes. 
 
Methods: 
TranS-C will be implemented in at least ten counties in California, United States (N = 96 providers; N = 
576 clients) via facilitation. CMHC sites are cluster-randomized by county to Adapted TranS-C or 
Standard TranS-C. Then, within each county, patients are randomized to immediate TranS-C or usual care 
followed by delayed treatment with TranS-C (UC-DT). Aim 1 will compare TranS-C (combined Adapted 
and Standard) with UC-DT on improvements in sleep and circadian problems, functional impairment, and 
psychiatric symptoms. Sleep and circadian problems will also be tested as a mediator between treatment 
condition (combined TranS-C versus UC-DT) and functional impairment/psychiatric symptoms. Aim 2 
will evaluate whether Adapted TranS-C is superior to Standard TranS-C with respect to provider 
perceptions of fit. Aim 3 will evaluate whether the relation between TranS-C treatment condition 
(Adapted versus Standard) and patient outcomes is mediated by better provider perceptions of fit in the 
Adapted condition. Exploratory analyses will: (1) compare Adapted versus Standard TranS-C on patient 
perceptions of credibility/improvement and select PhenX Toolkit outcomes; and (2) evaluate possible 
moderators. 
 
Discussion: 
This trial has the potential to (a) expand support for TranS-C, a promising transdiagnostic treatment 
delivered to patients with SMI in CMHCs, (b) take steps toward addressing challenges faced by providers 
in delivering EBPTs (i.e., high caseloads, complex patients, poor fit), and (c) advance evidence on causal 
strategies (i.e., adapting treatments to fit context) in implementation science. 
 
Trial registration: 
Clinicaltrials.gov identifier: NCT04154631. Registered on November 6, 2019. 
https://clinicaltrials.gov/ct2/show/NCT04154631 
 
Keywords: Transdiagnostic, sleep, circadian, serious mental illness, implementation, adaptation, 
community mental health 
 
 
 
 

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Table 1. SPIRIT Depiction of Timing and Measures Collected for Implementation Phase 
 
 
Screening 
Post-
Training 
Pre-
Treatment 
Mid-
Treatment 
Post-
Treatment 
6-Months 
Post-
Treatment 
Patient 
Sociodemographics 
 
 
x 
 
x 
x 
Eligibility Items 
x 
 
 
 
 
 
PROMIS-SDP 
x 
 
x 
x 
x 
x 
PROMIS-SRI 
 
 
x 
x 
x 
x 
DSM-5 Cross-Cutting 
 
 
x 
 
x 
x 
SDS 
 
 
x 
 
x 
x 
Sleep Health Composite 
 
 
x 
x 
x 
x 
PHENX Toolkit 
 
 
x 
 
x 
x 
CEQ 
 
 
 
 
x 
 
Provider 
Sociodemographics 
 
x 
 
 
 
 
Occupation 
 
x 
 
 
 
 
AcceptabilityP 
 
x 
 
x 
x 
 
Appropriateness 
 
x 
 
x 
x 
 
Feasibility 
 
x 
 
x 
x 
 
Number of Sessions 
 
 
 
 
x 
 
 
Note. Allocation to Adapted or Standard TranS-C occurs at the county level and prior to enrollment of any 
participants in that county (i.e., patients or providers). Enrollment of patients and allocation to immediate 
TranS-C or delayed TranS-C (UC-DT) occur after the screening and before the pre-treatment 
assessment. Enrollment of providers after the training.  P = Primary Outcome. PROMIS-SD = PROMIS-
Sleep Disturbance; note: PROMIS-SD is only assessed during the pre-treatment assessment if done 
more than one month after the screening to minimize burden for patients. PROMIS-SRI = PROMIS-Sleep 
Related Impairment. SDS = Sheehan Disability Scale. CEQ = Credibility/Expectancy Questionnaire
 
 

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Figure 1. Community Mental Health Center (CMHC) Randomization and Patient 
Timeline 
 
 
 
 
Standard 
Condition 
CMHC 
Randomized 
UC-DT 
(8 weeks) 
Start 
TranS-C 
Adapted 
Condition 
Pre-
Treatment 
Assessment 
Mid-
Treatment 
Assessment    
Post-
Treatment 
Assessment 
6-Month 
Follow-Up 
Assessment 
Post UC-DT 
Assessment 
UC-DT 
(4 weeks) 
Start 
TranS-C 
Pre-
Treatment 
Assessment 
Mid-
Treatment 
Assessment    
Post-
Treatment 
Assessment 
6-Month 
Follow-Up 
Assessment 
Primary facilitation activities 
Replicating 
Effective 
Programs 
Stages 1 and 2 
Post UC-DT 
Assessment 

8 
 
 
Figure 2. Provider Timeline 
 
 
 
 
TranS-C 
Training 
CMHC 
Randomized 
Begin 
Treatment 
Post-
Training 
Assessment 
Mid-
Treatment 
Assessment 
Post-
Treatment 
Assessment 

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1 BACKGROUND 
 
Serious mental illness (SMI) can have devastating psychosocial and health consequences [1-3]. SMI can 
be operationalized based on Public Law 102-321, the National Institute of Mental Health, and prior 
research as the presence of at least one psychiatric disorder that leads to substantial interference with one 
or more major life activities [4, 5]. Unfortunately, patients with SMI treated in routine practice settings 
too often do not receive evidence-based psychological treatment (EBPT) [6-8]. Indeed, although there has 
been a proliferation in EBPTs, only a small fraction of EBPT research is translated into routine practice 
settings [9, 10].  
 
Thus, large-scale implementation of EBPTs could have a tremendous positive impact. However, barriers 
have been identified. One barrier is that the context for implementation (e.g., routine practice settings) 
typically differs from the context in which EBPTs are developed (e.g., academic institutions). This causes 
poor “fit,” operationalized herein as provider perceptions of EBPT acceptability, appropriateness, and 
feasibility within their routine practice setting [11-14]. Importantly, fit predicts a host of implementation 
outcomes, including reach, treatment fidelity, and sustained use of treatments [15-17]. 
 
In the present study, we sought to determine if fit of an EBPT for SMI could be improved in the context 
of community mental health centers (CMHCs). CMHCs are major, publicly funded providers of treatment 
for SMI. CMHC providers carry heavy caseloads with high rates of comorbidity and complexity [18, 19]. 
Although it is not uncommon for CMHC providers to receive training in EBPTs, poor fit between EBPTs 
and CMHC contexts has contributed to infrequent use or even discontinuation [16, 20-22].  
 
A related challenge for CMHC providers and SMI treatment is that, in the proliferation of EBPTs, many 
focus on treating a single diagnosis [23]. Consequently, CMHC providers—and indeed providers in most 
routine practice settings—would need to learn and use several different treatments to address the various 
diagnoses of their patients. Given high caseloads and provider perceptions that EBPTs are more time-
consuming to learn and use than treatment as usual [21], these single-diagnosis treatments may reflect a 
significant barrier to implementing EBPTs.  
A promising alternative to single-diagnosis treatments is the transdiagnostic approach to treatment. The 
transdiagnostic approach holds that disorders co-occur in part due to common processes that drive 
symptoms [24-26]. By targeting these common processes, transdiagnostic treatments can address causes 
and symptoms of multiple disorders with a single protocol. In other words, transdiagnostic treatments 
may represent a path by which CMHC providers could use a single EBPT to help patients with a range of 
mental health problems.  
 
Circling back to SMI treatment in CMHCs, a question arises: what common mechanisms could be 
targeted by a transdiagnostic treatment to address a range of SMI diagnoses? Four lines of evidence 
highlight sleep and circadian problems as an important transdiagnostic contributor to SMI. First, sleep 
and circadian problems, including insomnia, hypersomnia, and evening circadian preference, predate and 
predict a range of mental illness, including depression, substance use, anxiety, and psychosis [27-32]. 
Second, common cognitive (e.g., worry, rumination), behavioral (e.g., avoidance), and biological (e.g., 
dopaminergic and serotonergic) processes may predict and maintain both SMI and sleep and circadian 
problems [26, 33, 34]. Third, treatments that focus on sleep and circadian problems are concurrently 
associated with improvements in mental health symptoms [35-37]. Fourth, there may be substantial 
heterogeneity in comorbidities between SMI and sleep and circadian problems in routine practice settings. 
For instance, in a CMHC sample, participants with schizophrenia spectrum disorder (n = 50), bipolar 
disorder (n = 35), and major depressive disorder (n = 26) exhibited 25, 24, and 21 distinct patterns of 
sleep and circadian problems, respectively [19]. Moreover, over 85% of the sample met criteria for at 
least one comorbidity between sleep and circadian problems, and over 80% met criteria for one or more 
sleep or circadian problems at the subdiagnostic level. Of note, number of sleep or circadian problems—

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including those at the subdiagnostic level—was more predictive of impairment than diagnostic threshold 
of those problems [38]. Collectively, these lines of research support the potential value of multi-problem, 
transdiagnostic interventions [39]. 
 
Building on this evidence, the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-
C) was developed [40]. Based on the Sleep Health Framework [41], TranS-C is transdiagnostic in two 
ways: it targets a range of sleep and circadian problems for individuals with a range of SMI diagnoses. 
TranS-C is a psychosocial, skills-based, and modular approach, consisting of (a) four core modules that 
form the basic building blocks of sleep health, (b) four cross-cutting interventions used in every session 
(e.g., motivational enhancement), and (c) seven optional modules that can be integrated based on case 
conceptualization, patient goals, and clinical judgment (see Method for details). 
 
The preliminary evidence for TranS-C in CMHCs has been promising. Specifically, among adult CMHC 
patients, TranS-C relative to usual care followed by delayed treatment with TranS-C (UC-DT) was 
associated with improvements in sleep and circadian problems, functional impairment, and psychiatric 
symptoms [35]. However, the providers delivering TranS-C were employed, trained, and supervised 
within an academic setting. The National Institute of Health's stage model holds that a critical next step 
would be to test TranS-C in a community setting with CMHC providers [42]. This is particularly 
important, because CMHC providers are responsible for the day-to-day delivery of clinical services, 
including EBPTs. Thus, ensuring that TranS-C is acceptable to CMHC providers, as well as effective for 
their patients, is essential. There is already promising evidence that cognitive behavior therapy for 
insomnia can be delivered in routine practice [43], including by non-specialists [44, 45], and the present 
study will build on these findings. 
 
Pertinent to this next step, TranS-C as it is typically delivered—henceforth “Standard TranS-C”—
involves relatively high-intensity procedures similar to other EBPTs. Specifically, TranS-C consists of 
eight weekly, 50-minute sessions. Indeed, in a process evaluation, CMHC staff identified dose and 
complexity as barriers to implementing Standard TranS-C [46]. In response, guided by the Replicating 
Effective Programs framework [47], our team developed a modified version of TranS-C—henceforth 
“Adapted TranS-C”—to improve fit with the CMHC context (see Method for systematic development of 
Adapted TranS-C). 
 
2 AIMS 
 
Together, the goal of the present hybrid type 2 effectiveness-implementation study [48, 49] is to evaluate 
the implementation and effectiveness outcomes of TranS-C in CMHCs of counties across California in 
the United States. The study will be conducted in three phases. This protocol will focus on Phase 1, the 
Implementation Phase, during which TranS-C is implemented in CMHCs via facilitation (see Method). 
Phase 2 is the Train-the-Trainer Phase, during which CMHC providers learn to train and supervise their 
peers in the delivery of TranS-C. Phase 3 is the Sustainment Phase, during which we will assess the 
extent to which TranS-C activities are sustained after facilitation has ceased.  
During the Implementation Phase, sites are cluster-randomized by county to Adapted TranS-C or 
Standard TranS-C with 1:1 allocation. Then, within each county, patients are randomized to immediate 
TranS-C or UC-DT. The first aim is to assess the effectiveness of TranS-C, compared to UC-DT. We 
hypothesize that, compared to UC-DT, TranS-C (combined Adapted and Standard) will be associated 
with larger reductions in the primary patient outcome of sleep disturbance and the secondary patient 
outcomes of sleep-related impairment, functional impairment, and psychiatric symptoms. We also 
hypothesize that TranS-C’s benefits for functional impairment and psychiatric symptoms will be mediated 
by improvements in sleep and circadian problems. The second aim is to evaluate whether TranS-C 
treatment condition (Adapted versus Standard TranS-C) is associated with fit to the CMHC context, 
operationalized as provider ratings of acceptability, appropriateness, and feasibility. We hypothesize that 

11 
 
 
Adapted TranS-C will be superior to Standard TranS-C with respect to the primary provider outcome of 
acceptability and the secondary provider outcomes of appropriateness and feasibility. The third aim is to 
evaluate whether better fit mediates the relation between TranS-C treatment condition and patient 
outcome. We hypothesize that relative to Standard TranS-C, Adapted TranS-C will be associated with 
greater reductions in the primary and secondary patient outcomes indirectly through higher provider 
ratings of acceptability, appropriateness, and feasibility. Exploratory analyses will: (1) compare Adapted 
and Standard TranS-C on patient perceptions of credibility/improvement and select PhenX Toolkit 
outcomes; and (2) determine whether treatment effects are moderated by risk factors including age, sex, 
and sleep and circadian and psychiatric symptoms at baseline [50, 51]. 
 
3 METHODS/DESIGN 
 
This study was preregistered on clinicaltrials.gov (identifier: NCT04154631) and received approval from 
the Committee for the Protection of Human Subjects at the University of California, Berkeley. Any 
protocol changes will be submitted to clinicaltrials.gov and the Committee for the Protection of Human 
Subjects. The research team will communicate relevant changes to the CMHCs and in appropriate 
publications (e.g., see subsection on Changes to Preregistration below). If there are too many findings to 
reasonably interpret in one paper, we may separate some of the findings into two or more papers. This 
research is funded by the National Institute of Mental Health (R01MH120147). The present protocol used 
the SPIRIT reporting guidelines [52] (see SPIRIT checklist in supplemental documents and Figures 1 and 
2). 
 
3.1 Facilitation 
 
In the present research, facilitation was selected as the core implementation strategy used to implement 
TranS-C in the CMHCs, based on promising evidence [53-55]. Facilitation has been defined as “multi-
faceted interactive process of problem solving, enabling and supporting individuals, groups, and 
organizations in their efforts to adopt and incorporate innovations into routine practices” [56]. This 
approach is grounded in the Promoting Action on Research Implementation in Health Services (PARiHS) 
framework [57]. In practice, each CMHC receives direct support from the lead facilitator, who is a 
licensed clinical social worker with expertise in community mental health and sleep treatment (ERA), and 
a team of trained facilitators employed by the research team. Throughout the study, the facilitation team is 
supervised by the Principal Investigator (PI; AGH) with periodic check-ins with an REP and facilitation 
expert (AMK). Team activities are also informed by the Veterans Affairs facilitation manual [58] and 
Harvey and Kitson’s (2015) Facilitation Guide. Additionally, the lead facilitator (ERA) and postdoctoral 
scholar (LDS) completed the Behavioral Health Veterans Affairs Quality Enhancement Research 
Initiative Implementation (BH QUERI) Facilitation Training and regularly attended BH QUERI’S 
monthly drop-in consultation group. 
 
The overarching approach of the facilitators is to conduct ongoing assessments for each CMHC site and 
then plan responses to identify unmet needs and mitigate barriers. This is accomplished via integrated 
bundles of evidence-based implementation strategies informed by implementation science [59, 60].  
 
The specific activities of the facilitators include the following. First, they organize and lead regular 
TranS-C trainings for interested CMHC providers. Second, the facilitators distribute treatment manuals 
and workbooks to all participating providers and patients. Third, drop-in supervision is offered once a 
week separately for each condition by the lead facilitator (ERA) and a clinical science graduate student 
(CAC) with expertise in TranS-C and SMI. Fourth, consultation is provided on an ongoing, as-needed 
basis by the facilitators via phone calls, text messaging, and emails. Fifth, the facilitators organize and 
lead regular presentations to the CMHCs on advanced topics related to sleep and mental health (e.g., 

12 
 
 
Lunch & Learn, Coffee Colloquium, Booster Sessions). Sixth, facilitators help with administrative 
barriers, such as working to ensure that TranS-C trainings count toward Continuing Education credits. 
Seventh, facilitators offer a sleep treatment certification that CMHC providers can achieve via three 
supervised TranS-C cases. Eighth, facilitators maintain an active website with supplemental treatment-
related resources for CMHC providers. Ninth, facilitators develop and distribute materials (e.g., flyers, 
educational videos, social media posts) related to sleep knowledge and mental health at the request of 
CMHC leadership and providers. Tenth, facilitators hold regular and as-needed meetings with CMHC 
leadership and key providers to provide progress updates, collaborate on decision-making, and problem-
solve barriers to effective intervention delivery such as organizational burnout, site cultures that are 
resistant to change, lack of resources or funding, oversaturation of treatment options, and logistical 
barriers. Eleventh, facilitators identify and cultivate intervention “champions” who can model effective 
treatment delivery and support their colleagues in using Trans-C. 
 
3.2 Participants 
 
Participants in the present study are drawn from CMHCs and consist of CMHC providers and CMHC 
patients1. The inclusion criteria for selecting the CMHC sites within counties from which to recruit 
providers and patients are: 1) provision of publicly funded adult mental health outpatient services and 2) 
support from CMHC leadership.  
 
CMHCs preferred to determine which providers are eligible to receive TranS-C training at each site (e.g., 
case managers, nurses, psychiatrists), because this aligns with their real-world practice. The other 
inclusion criteria for providers are: 1) employed or able to deliver client-facing services to patients within 
the CMHC; 2) interest in learning and delivering TranS-C; and 3) volunteer to participate and formally 
consent to participate.  
 
The inclusion criteria for patients are: 1) aged 18 years and older; 2) meet criteria for an SMI per self-
report and confirmed by referring provider or administration of the Mini International Neuropsychiatric 
Interview (DSM-5, Version 7.0.0) by a licensed clinical social worker on the research team; 3) exhibit a 
sleep or circadian disturbance as determined by endorsing 4 (quite a bit) or 5 (very much), or the 
equivalent for reverse scored items, on one or more PROMIS-Sleep Disturbance questions [61, 62]; 4) 
guaranteed place to sleep for at least two months that is not a shelter; 5) receiving the standard of care for 
the SMI and consent to regular communications between the research team and provider; and 6) consent 
to access their medical record and participate in assessments.  
 
Patients will be excluded if they meet any of the following criteria: 1) presence of an active and 
progressive physical illness or neurological degenerative disease that is directly related to the onset and 
course of the sleep and circadian problems, or making participation in the study unfeasible, as assessed by 
the Checklist of Medical Conditions and Symptoms on the Duke Structured Interview for Sleep Disorders 
[63] and clinical interview; 2) presence of substance abuse/dependence only if it makes participation in 
the study unfeasible; 3) current active intent or plan to commit suicide (those with suicidal ideation are 
eligible) only if it makes participation in the study unfeasible, or homicide risk; 4) night shift work for 
more than two nights per week in the past three months (i.e., regularly scheduled work from 12 a.m. – 6 
a.m.); or 5) pregnant or breastfeeding. 
 
 
1Note that the vast majority of providers and patients were employed by or seeking/receiving treatment at CMHCs. 
However, in very few isolated instances, providers and patients outside of CMHCs learned about the study (e.g., by 
word of mouth) and requested to participate. When the providers or patients otherwise met the criteria, they were 
permitted to participate, and were matched with a CMHC patient or provider, respectively, by the facilitation team. 

13 
 
 
3.3 Recruitment 
 
3.3.1 Community Mental Health Centers 
 
Building the CMHC network that forms the basis for this study began in August 2013 with outreach by 
the PI. The network has been maintained via newsletters, meetings, and workshops on EBPTs. Originally, 
eight counties agreed to participate. At various stages of the study, we have continued to focus on 
recruiting new counties and new CMHC sites to maximize provider and patient sample size goals. Most 
counties consist of three to 10 CMHC sites. Sites in the following ten counties in California, United States 
are currently participating in the Implementation Phase: Alameda, Contra Costa, Kings, Monterey, Placer, 
Santa Cruz, Solano, Santa Clara2, Santa Barbara, and Lake. Note that sites in San Louis Obispo are also 
participating but are operating as part of Monterey County. 
 
3.3.2 Providers 
 
Facilitators meet with key CMHC leadership, who help to engage and recruit providers in their CMHC. In 
some CMHCs, this involves leadership requiring all staff to complete the TranS-C training, whereas in 
other CMHCs, leadership advertises the opportunity and allows anyone who is interested to register. 
During the TranS-C trainings, facilitators continue to engage and recruit providers by describing the 
benefits of participating in the study. These benefits include: possible improvement in sleep and mental 
health for patients, certification in TranS-C for providers, expert consultation from the UC Berkeley 
research team, hard copies of the treatment materials, enrollment prizes, and financial compensation 
received by participating patients. After TranS-C trainings, facilitators follow up with weekly emails for 
one month that highlight each of these benefits and present other resources related to TranS-C, sleep, and 
mental health. Providers are also recruited through flyers posted in CMHCs, announcements at staff 
meetings, meetings organized by the facilitators, and appointments by leadership. Strategies to maintain 
relationships with providers and optimize data collection are ongoing by facilitators, including workshops 
and trainings, “enrollment challenges” and prizes (e.g., treatment-related books, magnets, t-shirts, mugs, 
and gift cards), continuing education credits for participation, and distributing newsletters or other topical 
resources. 
 
3.3.3 Patients 
 
Patients are recruited through a variety of methods, based on each CMHC’s preference. These methods 
include the following: (1) posting flyers from the research team in waiting rooms and providers’ offices; 
(2) integrating a sleep screener into intake paperwork; (3) asking providers to screen patients on their 
caseload; and (4) encouraging word of mouth between patients. Potentially eligible patients are typically 
identified by their provider. With the patient’s consent, the provider contacts the facilitators, who connect 
the patient with the assessment team so that the patient can be formally evaluated for eligibility and 
enrolled in the study. After eligibility has been confirmed and consent to participate in the study has been 
given, the patient is matched to a CMHC TranS-C provider. Ideally, the TranS-C provider is the patient’s 
own provider (e.g., their case manager, nurse, physician). If this is not possible, an alternative provider is 
identified. Patient retention is maximized via collaborative efforts between the providers, facilitators, and 
assessment team. Considerable efforts are made by the facilitators and assessors to answer questions and 
troubleshoot challenges (e.g., scheduling difficulties) to prevent attrition. 
 
 
2The site participating in Santa Clara is technically a federally qualified health center (FQHC), not a CMHC. 
Although there are some differences (e.g., funding structure, types of staff), these two types of sites are similar. 
Thus, for the purposes of this study, they are grouped together under “CMHC.” 

14 
 
 
3.4 Interventions 
 
Two variations of TranS-C are tested in the present research: Standard TranS-C and Adapted TranS-C. 
Both are delivered alongside the usual care offered by each CMHC. The control condition is usual care 
followed by delayed treatment with Adapted or Standard TranS-C (UC-DT). In the CMHCs, usual care 
consists of working with a service provider (e.g., psychologist, case manager, occupational therapist, 
psychiatrist, nurse practitioner) who provides direct mental health support from within their scope of 
practice. The patient might also be referred by that provider for other services as needed (e.g., healthcare, 
housing support, nutrition, vocational specialists, or peer advocacy). Occasionally patients receive 
treatment from interdisciplinary or residential teams, meaning their services are coordinated across 
multiple service providers. Although most providers delivered TranS-C via individual sessions, some 
chose to deliver it in a group setting. Note that TranS-C was originally developed in English, then 
translated into Spanish about four months into data collection to expand access and subsequently offered 
by Spanish-speaking providers. The treatment conditions, along with the adaptation process for Adapted 
TranS-C, are described below. 
 
3.4.1 Standard TranS-C 
 
Standard TranS-C is delivered by CMHC providers across eight 50-minute, weekly sessions [40]. It is 
comprised of 4 cross-cutting modules featured in every session, 4 core modules, and 7 optional modules 
that are used based on clinical presentation, treatment goals, and provider case conceptualization. The 
cross-cutting modules are case formulation, sleep and circadian education, motivational enhancement, and 
goal setting. Core module 1 targets irregular sleep-wake times, difficulty winding-down, and difficulty 
waking-up. Core module 2 aims to reduce daytime impairment. Core module 3 focuses on unhelpful 
beliefs about sleep. Core module 4 aims to promote maintenance of changes made during treatment. 
Optional module 1 addresses poor sleep efficiency via stimulus control [64] and sleep restriction [65]. 
Optional module 2 helps patients reduce time in bed. Optional module 3 addresses delayed or advanced 
phase problems (e.g., going to sleep later than desired or waking up earlier than desired). Optional module 
4 helps patients manage worries about sleep. Optional Module 5 promotes compliance with Continuous 
Positive Airways Pressure (CPAP) for patients with sleep apnea. Optional Module 6 helps patients 
negotiate sleep in complicated environments (e.g., noise from bed/roommates, traffic noise, streetlight 
entering the bedroom). Optional Module 7 is for patients who experience nightmares. Training for the 
Standard TranS-C condition consists of a 1-day workshop (i.e., 6-8 hours) or two, 3-hour training blocks. 
 
3.4.2 Adapted TranS-C 
 
Adapted TranS-C is delivered by CMHC staff across four, 20-minute, weekly sessions. Treatment 
consists of the same four cross-cutting and core modules as in Standard TranS-C, but the core modules 
are split up into five, rather than four, modules. Core module 1 targets irregular sleep-wake times. Core 
module 2 targets difficulty winding down. Core module 3 targets difficulty waking up. Core module 4 
aims to reduce daytime impairment. Core module 5 promotes maintenance of change. The one optional 
module focuses on reducing sleep-related worry and can be integrated with the core modules, based on 
clinical presentation, treatment goals, and provider case conceptualization. Training for the Adapted 
TranS-C condition consists of four, 1-hour workshops or two, 2-hour workshops, based on CMHC 
preferences. 
 
There have been calls for rigorous approaches to treatment adaptation [66, 67]. In response, we grounded 
the process for adapting TranS-C in theory, data, and stakeholder input. As the overarching guide for the 
adaptation process, the Replicating Effective Programs (REP) framework [47] was used. Phase 1 of REP 
(Pre-Condition) was completed prior to the present protocol. First, as discussed above, we established that 

15 
 
 
there is a need for effective, feasible EBPTs for SMI in CMHCs and that sleep and circadian functioning 
may represent a powerful target to help address this need. Second, we determined that there was empirical 
support for TranS-C in CMHCs [35]. Third, we gathered stakeholder input on fit and packaging of the 
intervention [46, 50]. Fourth, we reviewed past data and identified the TranS-C treatment skills that were 
most utilized by patients with a utilization scale adapted from [68]. Fifth, we considered TranS-C’s 
theoretical underpinnings and mechanisms of action [40, 41] from which we retained the core elements 
[66, 69]. Sixth, we piloted Adapted TranS-C with 21 adults through the PI’s UC Berkeley research clinic 
(unpublished data). Informal feedback was solicited from providers and patients who participated in this 
pilot to further refine Adapted TranS-C. In Phase 2 of REP (Pre-Implementation), we customized the 
delivery of TranS-C training and treatment materials to the CMHC context based on the input from 
CMHC leadership, staff, and patients [46, 50]. Throughout REP Phases 1 and 2, following leading 
adaptation frameworks, we sought to ensure that Adapted TranS-C would be relevant to the broadest 
range of patients and to account for factors that impact implementation (e.g., resources required) [66, 70, 
71]. The present trial will address the last two phases of REP – namely, Phases 3 (Implementation) and 4 
(Maintenance and Evolution). 
 
3.4.3 UC-DT 
 
In UC-DT, patients begin with usual care for four weeks if their CMHC is randomized to Adapted TranS-
C and eight weeks of usual care if their CMHC has been randomized to Standard TranS-C. After the 
delay, they receive Adapted or Standard TranS-C, also based on the condition to which their CMHC has 
been randomized (see Figure 2). The decision to compare TranS-C to UC-DT was made in close 
collaboration with the early CMHC partners. This design aims to strike a balance between (a) including a 
comparison group to demonstrate the effectiveness of TranS-C in community settings; (b) ensuring that 
all participants receive what we hypothesize to be an active treatment (TranS-C); and (c) maximizing 
efficiency in terms of study duration, budget, and participants’ time investment. Notably, usual care has 
been the comparison group in several influential studies [72-74]. 
 
3.5 Measures 
 
In addition to the measures below, a sociodemographics form is completed by providers and patients. 
Only measures that will be analyzed for the primary aims of the Implementation Phase (see above) are 
reported below. See Table 1 for timing of each measure. 
 
3.5.1 Providers 
 
3.5.1.1 Primary Outcomes 
 
Acceptability. Providers rate the acceptability of TranS-C via the Acceptability of Intervention Measure 
[75]. This 4-item measure is rated on a scale from 1 (completely disagree) to 5 (completely agree). This 
measure has demonstrated satisfactory known-groups validity, internal reliability, test-retest reliability, 
and sensitivity to change [75]. 
 
3.5.1.2 Secondary Outcomes 
 
Appropriateness and Feasibility. Providers rate the appropriateness and and feasibility of TranS-C via the 
following 4-item measures: Intervention Appropriateness Measure and Feasibility of Intervention 
Measure [75]. Both measures are rated on a scale from 1 (completely disagree) to 5 (completely agree). 
These measures have demonstrated satisfactory known-groups validity, internal reliability, test-retest 
reliability, and sensitivity to change [75].   

16 
 
 
 
3.5.1.3 Other Measures 
 
Number of TranS-C Sessions. The number of sessions delivered to each enrolled patient by each provider 
will be counted. 
 
Occupation. Providers are asked to report their current position, professional degree, and work history, 
including their caseload, theoretical orientation, licensure status, and previous training in sleep treatment. 
 
3.5.2 Patients 
 
3.5.2.1 Primary Outcomes 
 
Sleep Disturbance. The 8-item PROMIS-Sleep Disturbance (PROMIS-SD) assesses disruption to sleep 
(e.g., restlessness, trouble staying asleep) over the past seven days [62]. Items are rated on a scale from 1 
(not at all/never/very poor) to 5 (very much/always/very good), and scores range from 8-40, with higher 
scores indicating greater disturbance. This measure has demonstrated acceptable reliability and validity 
[61, 62]. 
 
3.5.2.2 Secondary Outcomes 
 
Sleep-Related Impairment. The 16-item PROMIS-Sleep Related Impairment (PROMIS-SRI) assesses 
daytime impairment related to sleep problems over the past seven days on a scale from 1 (not at all/never) 
to 5 (very much/always) [62]. Scores range from 16-80, with higher scores indicating greater impairment 
(e.g., daytime sleepiness, difficulty concentrating). This measure has demonstrated excellent 
psychometric properties [61, 62].  
 
Functional Impairment. Functional impairment is assessed via the Sheehan Disability Scale (SDS) [76]. 
Impairment in work and school, social life, and home and family is rated on a scale from 0 (not at all) to 
10 (extremely). Scores range from 0-30, with higher scores indicating greater impairment. This measure 
has demonstrated good reliability and validity [76, 77]. 
 
Overall Sleep Health. The Sleep Health Composite is proposed to capture overall sleep health for the 
complexity of sleep problems in SMI that are covered by TranS-C [78]. It is defined as the sum of scores 
on six sleep health dimensions (each dimension dichotomized as 1 = good; 0 = poor): Regularity 
(midpoint fluctuation), Timing (mean midpoint), Efficiency (sleep efficiency), Duration (total sleep time), 
Satisfaction (sleep quality question on PROMIS-SD), and Alertness (daytime sleepiness question on 
PROMIS-SRI). All dimensions – except Satisfaction and Alertness – are assessed via questions about 
sleep-wake patterns over the past seven days (e.g., In the past week, what time have you usually woken up 
in the morning?). Scores range from 0-6, with higher scores indicating better sleep health. Initial validity 
of this measure has been established [78].  
 
Psychiatric Symptoms. The DSM-5 Cross-Cutting Measure assesses psychiatric symptoms across 13 
mental health domains. Participants rate how often they were bothered by each symptom on a scale from 
0 (not at all) to 4 (nearly every day). Scores range from 0-52, with higher scores indicating more severe 
symptoms. This measure has demonstrated good test-retest reliability and clinical utility [79, 80]. 
 
3.5.2.3 Exploratory Outcomes 
 
PhenX Toolkit: Substance Use and Suicidality. Scales from the PhenX Toolkit [81] are used to assess 
various patient outcomes, including suicidal ideation and behaviors, alcohol, tobacco, and other 

17 
 
 
psychoactive substances (e.g., cannabis, hallucinogens, sedatives, etc.). PhenX measures have been 
compiled by working groups and domain experts via a consensus process to facilitate consistency across 
studies [81]. To assess suicidal ideation and behaviors, the PhenX ‘Classification of Suicidal Ideation and 
Suicidal Behavior – Adult – Current’ protocol is used. This protocol includes two subscales from the 
screening version of the Columbia-Suicide Severity Rating Scale: Severity of Suicidal Ideation and 
Suicidal Behavior, assessing suicidality during two time periods—namely ideation in the past month and 
suicidal behavior in the past three months. To ease patient burden, this measure was adapted slightly, such 
that if patients deny suicidal ideation, they ae not required to answer questions about suicidal behavior. To 
assess alcohol, the PhenX ‘Alcohol – 30-Day Quantity and Frequency’ protocol is used. This protocol 
measures both quantity and frequency of alcohol consumption. To assess tobacco, the PhenX ‘Tobacco – 
30-Day Quantity and Frequency – Adult’ protocol is used. This measure has three sets of question 
protocols: (1) a protocol for ‘Every-Day Smokers,’ (2) a protocol for ‘Some-Day Smokers,’ and (3) a 
protocol for ‘Former Smokers.’ If patients report that they have never smoked tobacco, this measure is 
skipped. To assess use of substances and other drugs, the PhenX ‘Substances – 30-Day Frequency’ 
protocol is used. This measure assesses use of substances such as sedatives, painkillers, stimulants and 
hallucinogens. In addition, caffeine is assessed using questions adapted from the ‘Supplemental Beverage 
Questionnaire.’ Questions used in the present study assess frequency and quantity of caffeinated or 
decaffeinated drinks consumed over the past 30 days. 
 
Credibility and Perceived Improvement. Perceptions of TranS-C credibility and symptom improvement 
are assessed by four questions adapted from the Credibility/Expectancy Questionnaire (CEQ) [82]. These 
questions assess (1) how logical TranS-C seemed, (2) how successful it was in reducing sleep symptoms, 
(3) how confident patients would be in recommending TranS-C to a friend, and (4) how much 
improvement patients believe had occurred. All questions are rated on a scale from 0 (not at all) to 9 
(very), except for perceived improvement, which is rated as a percentage from 0-100%. 
 
3.6 Procedure 
 
Providers and patients are consented by the assessment team prior to participation. All participants are 
informed that they can withdraw from the study at any time. All patients are compensated for their 
participation, and providers are compensated if permitted by their CMHC. The assessments are completed 
by the assessment team, comprised of experienced assessors. Note that assessors complete the consent 
process to minimize burden on participants (e.g., this practice reduces number of calls from team). 
Because the assessors need to provide study-related information—such as number of assessments and 
treatment sessions—to the patients during the consent process, the assessors are not blind to condition at 
pre-treatment. However, at post-treatment and 6FU, we endeavor to keep assessors blind to condition. As 
is common in clinical trials, there are ways that assessors may be able to infer treatment condition (e.g., 
slightly different assessment batteries, patients may ask assessors “when does treatment start?” during the 
post-delay assessment). Assessors receive ongoing supervision and are thoroughly trained to deliver the 
surveys with integrity and minimal bias. 
 
3.6.1 Providers 
 
Provider assessments are completed after TranS-C training, at mid-treatment, and at post-treatment. See 
Figure 4 for provider timeline. 
 
3.6.2 Patients 
 
Patient assessments in the immediate TranS-C treatment conditions are completed at pre-treatment, mid-
treatment, post-treatment, and six months after treatment (6FU). Patient assessments in the UC-DT 

18 
 
 
condition are completed at pre-treatment and four or eight weeks after pre-treatment (i.e., post UC-DT), 
depending on whether their county has been randomized to Adapted or Standard TranS-C, respectively. 
After the post UC-DT assessment, patients start delayed treatment with TranS-C. They subsequently 
complete assessments at mid-treatment, post-treatment, and 6FU. Note that patients do not complete a 
6FU assessment after the delayed portion of UC-DT. This was a compromise made with CMCH partners, 
so that patients would not need to wait 7-8 months to receive treatment. See Figure 2 for patient timeline. 
 
3.7 Allocation 
 
CMHCs and patients are randomized through a computerized randomization sequence. We do not stratify 
during randomizing at the county level. When randomizing patients, we stratify for presence of psychosis 
or not (current), presence of substance use or not (current) and age (≥ 50 or not), as there is evidence these 
variables can impact sleep or treatment outcome [50, 83, 84]. Only the facilitators, assessors, and research 
team (i.e., not CMHCs, providers, or patients) are privy to which CMHCs and patients are allocated to 
which TranS-C treatment condition (Adapted versus Standard TranS-C). CMHC providers and patients 
know whether their patients have been randomized to receive the immediate or delayed treatment. The 
facilitator informs each provider when they can start having sessions. In the immediate condition, the 
provider is asked to begin sessions as soon as possible. In the delayed condition, the provider is asked to 
wait until after the patient has completed the post-delay assessment (i.e., approximately four weeks in the 
Adapted condition or eight weeks in the Standard condition). 
 
3.8 Sample Size 
 
Sample size was determined via power analyses with Optimal Design [85, 86] and Stata 15 [87] and a 
two-sided alpha of 0.05. We used the original N = 8 counties that agreed to participate, though as noted 
above, new counties have since been recruited to meet sample size goals determined by the following 
calculations. For Aims 1 and 3, 60 patients from eight randomized clusters/CMHCs was found to provide 
over 90% power to detect large effects (average d=0.89 from [35]) between TranS-C and UC-DT, and 
over 80% power to detect a medium effect (d=0.50) between Standard and Adapted TranS-C. For Aim 2, 
based on a prior study with a similar aim/measure [88], we expected a large effect size (d=0.80) between 
Standard and Adapted TranS-C on the primary outcome of acceptability. Prior studies have reported high 
correlation coefficients between acceptability, feasibility, and appropriateness (rs=.67-.90) [75, 89], thus 
the analysis for acceptability was expected to sufficiently power the other outcomes. Based on the site 
intra-class correlation (ICC) estimated from similar prior studies [90, 91], we assumed the ICC to be 0.01 
and the inflation factor was calculated as 1+(n-1)xICC. Eight clusters with an average cluster size of 10 
was found to be needed to achieve over 80% power to detect a large effect for the outcome (acceptability) 
at the provider level. Thus, to test this aim, we would need at least 80 CMHC providers: 10 providers 
from 8 CMHCs (at least 4 CMHCs per randomized arm).  
In sum, we aimed to randomize at least eight CMHC clinics to either Standard or Adapted TranS-C, with 
at least 80 providers (10 per CMHC). The goal for patients is 240 patients (30 per CMHC) randomized to 
immediate Standard or Adapted TranS-C and 240 randomized to UC-DT (30 per CMHC). Adding 20% to 
account for attrition, the total target sample size for providers is 96 and for patients is 576. 
 
3.9 Data Management and Dissemination 
 
All patient-identifiable data are saved by the assessment team on password-protected fillable PDFs on a 
secure password-protected and HIPAA-compliant website. On these PDFs, patients and providers are 
assigned identification numbers. These identification numbers are then used to link anonymized data that 
is collected via password-protected Qualtrics. When collecting assessments, assessors call participants 
and enter the data into Qualtrics. Participants also have the option of entering their data directly into a 

19 
 
 
participant-facing version of the surveys via a HIPAA-compliant version of Qualtrics. Patient-identifiable 
data is not shared with outside entities during or after the trial. A data management team supervised by 
the PI, biostatistician (LD), and postdoctoral scholar (LDS) is responsible for downloading, collating, and 
analyzing the data. 
 
A Data Safety Monitoring Board has been formed to help prevent and manage adverse events. The board 
includes members with expertise in SMI, psychosocial treatments, and randomized controlled trials. 
Members are independent from the PI and competing interests. A report was made to the board bi-
annually for the first year of the research. Since then, it has shifted to annual reports. However, if safety 
issues arise, it will be changed to monthly meetings. Yearly reports are submitted to the Committee for 
the Protection of Human Subjects at UC Berkeley and National Institute of Mental Health. 
 
Outcomes specifically of interest to our partners are presented to CMHC leadership as part of the widely-
used implementation strategy, audit and feedback [92]. However, these interim analyses are used only for 
facilitation purposes. In other words, they do not influence research procedures in any way (e.g., to 
inform when to terminate the trial). 
 
Results from the trial, as well as analysis code, will be shared via peer-reviewed publications, professional 
conference presentations, and meetings and newsletters to CMHCs, as relevant. Other than the authors 
and compliance with data-sharing agreements stipulated by the National Institutes of Health, no other 
entities have contractual agreements to access the final dataset. Deidentified data are submitted to the 
National Institute of Mental Health Data Archive twice per year, per their requirements. 
 
3.10 Roles and Responsibilities 
 
This trial is supervised by the PI (AGH), who manages the facilitation team, assessment team, and the 
data management team. The PI meets with members of each team as needed in addition to daily email 
communication. Within each team, there is at least one trained lead (ERA, KF, JMS, LD, LDS) who 
supervises the day-to-day activities of other team members. There is no coordinating center, trial steering 
committee, or Stakeholder and Public Involvement Group. The responsibilities of each team are detailed 
elsewhere in this protocol. In summary, the facilitators execute the implementation of TranS-C via 
numerous activities, including training and supervising CMHC providers in the delivery of TranS-C. The 
assessment team is responsible for the informed consent process and conducting participant (i.e., provider 
and patient) evaluations. CMHC leadership and enrolled providers work with the facilitation team to 
recruit additional providers. CMHC providers help to identify potentially eligible patients, who are then 
connected with the assessment team for formal eligibility evaluation. 
 
3.11 Changes to Preregistration 
 
In December 2022, updates relevant to the present protocol were made to the clinicaltrials.gov protocol 
(identifier: NCT04154631). These updates can be summarized as follows. First, minor changes were 
made to four inclusion and exclusion criteria, based on feasibility and preferred practices of CMHC 
partners. For instance, as described above, CMHCs preferred to determine which providers were eligible 
to deliver TranS-C, and thus, providers held a wider range of positions than we had originally anticipated. 
Additionally, in rare cases, providers outside of CMHCs participated in the study (e.g., a supervisee from 
a CMHC provider’s private practice participated). Thus, we changed the provider criteria to be more 
inclusive (see provider inclusion criterion 1 above). Second, as the study unfolded, rich data emerged. To 
maximize the resources invested in this study, we decided to systematically capture these data with 
additional measures, now included in the ‘Other Measures’ section of clinicaltrials.gov. For example, the 
number of sessions delivered to each enrolled patient per provider was already being logged by the 

20 
 
 
facilitators. These data will be included in the dropout calculations and sensitivity analyses of the 
Implementation Phase main aims (see Measures and Planned Analyses). Third, as described above, to 
maximize recruitment goals, additional CMHC sites were recruited from new counties on an ongoing 
basis. We have added these additional sites/counties to clinicaltrials.gov. Fourth, the sample size has been 
changed to match the power analysis described above. The sample size in the original clinicaltrials.gov 
preregistration reflected a request from one reviewer of the National Institute of Mental Health grant to 
increase the sample size to account for a higher rate of dropout. However, this was deemed to be 
unfeasible, and the recruitment goal was adjusted to reflect the power analysis. Fifth, the type of design 
was corrected to be hybrid type 2, given the equal emphasis on determining effectiveness of an 
intervention (i.e., TranS-C) and feasibility/impact of an implementation strategy (i.e., adapting 
interventions to fit context) [48, 49]. Sixth, after launching the study, we received requests to minimize 
burden for patients. In response, assessors began consenting patients during pre-treatment assessments. 
Thus, we now specify that assessors are blind to condition at post-treatment and 6FU. 
 
4 PLANNED ANALYSES 
 
4.1 Preliminary Analyses and Missing Data 
 
Analyses will use all available data (intent-to-treat) [93]. If dropout is related to other variables, they will 
be included as predictors. Baseline between-group differences on demographic variables will be 
examined. These tests will not be used to select covariates in the primary intention-to-treat analysis [94-
97]. Instead, covariates will be carefully selected at the conclusion of the trial—given the variations due 
to COVID-19 and the CMHC context (see Discussion)—and the potential influences of baseline 
differences will be evaluated as moderators (approach to moderation described below). Analyses 
comparing TranS-C to UC-DT will evaluate change in outcomes from pre-treatment to post-treatment. 
Analyses comparing Adapted to Standard TranS-C will evaluate change in outcomes from pre-treatment 
to post-treatment and pre-treatment to 6FU (see Method and Discussion for more details). 
 
Distributions will be evaluated to detect outliers, and we will ensure that assumptions of planned analyses 
are met. Covariates will include the patient variables for which we stratified (i.e., age and presence of 
psychosis or substance use). For all statistical models, counties will be entered as dummy variables rather 
than a level of analysis due to the relatively small number of clusters. The average intraclass correlation 
on provider and patient level outcomes will be reported. The Benjamini–Hochberg procedure [98] will be 
used to correct for multiple testing for confirmatory analyses on the primary outcomes (i.e., sleep 
disturbance and acceptability) [99]. 
 
4.2 Dropout 
 
The N by stage of dropout will be reported for the following: dropout after randomization but before the 
first treatment session, dropout after treatment has begun but attended half or fewer of the intended 
number of sessions (i.e., ≤ 2 in Adapted, ≤ 4 in Standard), dropout after attended more than half the 
intended number of sessions (i.e., > 2 in Adapted, > 4 in Standard) but before treatment has been 
completed, and dropout after treatment has been completed but prior to post-treatment or 6FU 
assessments. The number of patients who completed a post-treatment assessment but were lost to 6FU 
will also be reported. When available, the reasons for dropout and improvement among patients who drop 
out will be reported. 
 
4.3 Aim 1: Effectiveness Outcomes of Standard or Adapted TranS-C versus UC-DT 
 
Multilevel modeling (MLM) [100-102] will be used to account for multiple observations nested within 

21 
 
 
patient. The level 1 equation will include dummy-coded time indicators as the predictor (0 = pretreatment, 
1 = post-treatment). The level 2 equation will include dummy-coded treatment condition (0 = UC-DT, 1 = 
Adapted or Standard TranS-C), treatment by time interaction terms, and dummy-coded counties (0 = 
Alameda, 1 = Contra Costa, 2 = Kings, 3 = Monterey, 4 = Placer, 5 = Santa Cruz, 6 = Solano, 7 = Santa 
Clara, 8 = Santa Barbara, and 9 = Lake) as predictors. The treatment effects of interest will be significant 
treatment by time interactions at the 5% level on the primary outcome of sleep disturbance and the 
secondary outcomes of sleep-related impairment, functional impairment, and psychiatric symptoms, all 
modeled as continuous variables. Significant treatment by time interactions indicate that change in patient 
outcomes is significantly different comparing Adapted or Standard TranS-C to UC-DT. Significant 
interactions will be interpreted using planned contrasts (i.e., treatment effects on change from pre-
treatment to post-treatment) and graphs. Additionally, the indirect effects of TranS-C relative to UC-DT 
on functional impairment and psychiatric symptoms through improvements in sleep disturbance and 
sleep-related impairment will be tested using multilevel structural equation modeling [103]. 
 
4.4 Aim 2: Adapted TranS-C versus Standard TranS-C on Fit to CMHC Context 
 
MLM will be used to account for multiple observations nested within providers. TranS-C treatment 
condition (Adapted versus Standard TranS-C) will be evaluated as a predictor of fit, operationalized as 
provider ratings of acceptability, feasibility, and appropriateness. The level 1 equation will include 
dummy-coded time indicators as the predictor (0 = pre-treatment, 1 = post-treatment). The level 2 
equation will include dummy-coded treatment condition (0 = Standard TranS-C, 1 = Adapted TranS-C), 
treatment by time interaction terms, and dummy-coded counties (0 = Alameda, 1 = Contra Costa, 2 = 
Kings, 3 = Monterey, 4 = Placer, 5 = Santa Cruz, 6 = Solano, 7 = Santa Clara, 8 = Santa Barbara, and 9 = 
Lake) as predictors. The treatment effects of interest will be significant treatment by time interactions at 
the 5% level on the primary outcome of acceptability and the secondary outcomes of feasibility and 
appropriateness, all modeled as continuous variables. Significant treatment by time interactions indicate 
that change in perceptions of fit is significantly different comparing Adapted to Standard TranS-C. 
Significant interactions will be interpreted using planned contrasts (i.e., treatment effects on change from 
pre-treatment to post-treatment) and graphs. 
 
4.5 Aim 3: Fit as a Mediator of Treatment Condition and Patient Outcome 
 
Multilevel structural equation modeling will be used to test whether improved perceptions of fit (i.e., 
acceptability, appropriateness, and feasibility) mediate the relation between TranS-C treatment condition 
(i.e., Adapted versus Standard TranS-C) and change in the primary patient outcome of sleep disturbance 
and the secondary patient outcomes of sleep-related impairment, functional impairment, and psychiatric 
symptoms. Models will evaluate change in outcomes from pre-treatment to post-treatment and pre-
treatment to 6FU. 
 
4.6 Sensitivity Analyses 
 
Three sets of sensitivity analyses will be run to help account for the complexities of the COVID-19 
pandemic and the CMHC context. In the first set, the analyses for Aims 1-3 will be conducted with (a) 
treatment completers, (b) patients who completed more than half the number of the suggested sessions 
(i.e., > 2 sessions for Adapted and > 4 sessions for Standard), and (c) patients who completed half or 
fewer of suggested sessions. In other words, these analyses will test the effectiveness of TranS-C at 
varying doses, which may be important considering evidence on “early responders” [104] and “real 
world” contexts where turnover and dropout can be high [105, 106]. In the second set of sensitivity 
analyses, we will assess whether any patients who did not complete post-treatment or 6FU had achieved 
meaningful clinical improvement by mid-treatment, using a reliable change index for the primary 

22 
 
 
outcome of PROMIS-SD [107]. For the sensitivity analyses, we will define these patients as completers, 
and we will use their mid-treatment assessment in place of a post-treatment assessment. Then, all pre- to 
post-treatment analyses for Aims 1-3 will be rerun. In the third set of sensitivity analyses, we will run the 
analyses for Aims 1-3 but only include post-treatment and 6FU assessments that were collected within 3 
months of the target assessment date (e.g., a 6FU assessment that was completed nine months after 
treatment ended). 
 
4.7 Exploratory Aim 1: TranS-C Treatment Condition on Credibility and PhenX Toolkit 
 
MLM will be used to test TranS-C treatment condition (Adapted vs. Standard TranS-C) predicting PhenX 
Toolkit outcomes of substance use and suicidality at post-treatment and 6FU. The approach to MLM will 
mirror Aim 2, except the outcomes will be substance use and suicidality from the PhenX Toolkit. Linear 
regression will be used to test treatment condition (Adapted vs. Standard TranS-C) predicting patient 
perceptions of TranS-C’s credibility at post-treatment. 
 
4.8 Exploratory Aim 2: Treatment Effects Moderated by Risk Factors 
 
Using MLM, three-way interactions between treatment condition (Adapted or Standard TranS-C versus 
UC-DT), time, and risk factors will be used to evaluate moderators of patient outcome (i.e., sleep and 
circadian problems, functional impairment, and psychiatric symptoms). Each moderator and outcome will 
be tested in a separate model. Moderators will include age, sex, and sleep/circadian and psychiatric 
symptoms at baseline. The level 1 equation will include the moderator and dummy-coded time indicators 
as the predictors (0 = pre-treatment, 1 = post-treatment). The level 2 equation will include dummy-coded 
treatment condition (0 = UC-DT, 1 = Adapted or Standard TranS-C), treatment by time by moderator 
interaction terms, and dummy-coded counties (0 = Alameda, 1 = Contra Costa, 2 = Kings, 3 = Monterey, 
4 = Placer, 5 = Santa Cruz, 6 = Solano, 7 = Santa Clara, 8 = Santa Barbara, and 9 = Lake) as predictors. A 
significant interaction indicates a moderating effect and will be probed with planned contrasts (e.g., 
moderating effects on the differences between treatments in change from pre-treatment to post-treatment) 
and graphs. Simple slope analyses will be conducted for significant continuous moderators. 
 
5 DISCUSSION 
 
This study aims to evaluate implementation and effectiveness outcomes of the Transdiagnostic 
Intervention for Sleep and Circadian Dysfunction (TranS-C) delivered to patients in community mental 
health centers (CMHCs) by CMHC providers. Findings will address several research priorities. First, 
TranS-C was designed to target a wide range of serious mental illness (SMI) diagnoses through 
addressing a wide range of sleep and circadian problems [35, 40]. As such, TranS-C has the potential to 
improve symptoms of patients with diverse and complex clinical presentations, which may help alleviate 
provider burden and optimize patient care [18, 19]. Second, providers play a pivotal role in the day-to-day 
delivery of mental health care. Thus, it is critical to assess their perceptions of implemented treatments. 
The present findings will advance our understanding of providers’ perceptions of TranS-C’s ‘fit’ – 
namely, acceptability, feasibility, and appropriateness – which is an important predictor of 
implementation outcomes (e.g., fidelity, reach, sustainability; [15-17]). Third, leaders in the 
implementation science have advocated for causal tests of implementation strategies [108]. Heeding this 
call, the present study tests whether adapting TranS-C to CMHCs improves ‘fit’ between the treatment 
and context, and if so, whether this improved fit is associated with better patient outcomes. Evaluating 
this implementation strategy (i.e., adapting treatments to fit contexts) may help other implementation 
scientists evaluate whether it is a worthwhile implementation strategy to pursue. 
 
These potential contributions should be considered alongside the protocol’s methodological limitations. 

23 
 
 
First, we did not collect data from leadership at our partner CMHCs. The primary reason was that these 
leaders have many demands on their time and already had generously collaborated with us on the design 
and implementation of the study. However, we recognize that leadership-level factors can meaningfully 
impact implementation outcomes [109]. Thus, exploring leadership perspectives on TranS-C will be an 
important direction for future research. Second, we considered additional assessment methods for sleep 
and circadian problems, such as a diagnostic interview (e.g., the Duke Structured Interview for Sleep 
Disorders; [63]) and polysomnography. However, even without these more intensive diagnostic tools, 
some patients reported difficulty with the length of assessments. Additionally, we were interested in 
recruiting patients with a broad range of symptom severity, which may have more ecological validity than 
a more restricted sample. That said, we acknowledge that there are certain outcomes that we will not be 
able to address due to this design choice (e.g., change in diagnostic status). Third, assessors are not blind 
to treatment condition at pre-treatment. To minimize burden on participants (e.g., reduce number of calls 
from team), assessors are in charge of the consent process, and therefore, they need to provide study-
related information—such as number of assessments and treatment sessions—to the patients. At post-
treatment and 6FU, we endeavor to keep the assessors blind but, as is common in clinical trials, there are 
ways that assessors may be able to infer treatment condition (e.g., slightly different assessment batteries). 
In rare instances, patients in the UC-DT condition ask assessors questions such as “when does treatment 
start?” or “when will I receive the workbook?” during the post-delay assessment. In such instances, the 
assessor may be able to ascertain that the patient has been assigned to the UC-DT condition. Assessors 
receive ongoing supervision and are thoroughly trained to deliver the surveys with integrity and minimal 
bias. Additionally, relative to interviews or clinical rating scales, the standardized surveys delivered by 
assessors are likely less vulnerable to bias (see Measures above). However, it is possible that assessor 
knowledge or hunches about patients’ condition may influence patient responses. Fourth, in the UC-DT 
condition, patients did not complete a 6FU assessment after the delay. This was a compromise made with 
CMCH partners, so that patients would not need to wait 7 to 8 months to receive treatment. Thus, 
analyses comparing TranS-C with UC-DT will evaluate outcomes from pre- to post-treatment, whereas 
analyses comparing Adapted versus Standard TranS-C can evaluate outcomes from pre- to post-treatment 
and pre- to 6FU. Depending on the outcomes from the trial, an important direction for future research will 
be testing the long-term effects of TranS-C relative to a control (e.g., UC-DT). Fifth, unforeseen 
challenges arose related to the COVID-19 pandemic and subsequent mandates in California (e.g., shelter-
in-place). For some patients, sleep and circadian treatment became secondary to coping with other mental 
health ramifications of the pandemic (e.g., anxiety, isolation, grief) or even meeting basic needs of secure 
access to food and shelter [110]. Additionally, providers suddenly had more demands on their time, such 
as navigating the shift from in-person to virtual care—a shift that was also challenging for some patients. 
These changes had implications for the present study. For instance, we did not design the study to 
systematically evaluate potential differences in TranS-C delivery (i.e., in-person versus virtual versus 
phone). As another example, there was substantial turnover in providers and patients, leading to more 
discontinuity across treatment than anticipated. Sixth, some design choices were made to respect the 
expertise and preferences of our CMHC partners. For example, providers had the option to deliver TranS-
C in a group or individual format. Even though this introduced variance into the study, it has been critical 
to the CMHCs, providers, and patients that we balance rigor with flexibility. We deliberated about 
whether to control for these variations necessitated by COVID-19 and the CMHC contexts. Ultimately, 
we decided against a prespecifying a comprehensive list of possible covariates, given the statistical 
drawbacks of controlling for many variables (e.g., [94-97). At the conclusion of the trial, the sources of 
variation that resulted from the COVID-19 pandemic and needs/preferences of our community partners 
will be carefully considered as to whether they should be included as covariates. 
 
These challenges and limitations notwithstanding, in testing the implementation and effectiveness 
outcomes of TranS-C delivered by CMHC providers, this study has the potential to (a) expand support for 
a promising transdiagnostic treatment delivered to patients with SMI in routine practice settings, (b) take 
steps toward addressing some of the major challenges faced by providers in delivering evidence-based 

24 
 
 
treatments (i.e., high caseloads, complex patients, poor fit), and (c) advance evidence on causal strategies 
in implementation science. 
 
6 TRIAL STATUS 
Protocol version 1, December 12, 2022. Data collection started in February 2020. Recruitment was 
completed in August 2022, but patient visits/assessments will continue through August 2023. Publishing 
of this protocol was delayed because of unforeseen challenges and uncertainties related to the COVID-19 
pandemic and subsequent mandates (e.g., shelter-in-place), which began in California shortly after data 
collection started for this study. 
 
7 DECLARATIONS 
 
7.1 Ethics approval and consent to participate 
 
The Committee for the Protection of Human Subjects at the University of California, Berkeley approved 
this study (2019-04-12091). Written informed consent is obtained from all participants. 
 
7.2 Consent for publication 
 
Model consent forms are available upon request. 
 
7.3 Availability of data and materials 
 
Other than the authors and compliance with data-sharing agreements stipulated by the National Institutes 
of Health, no other entities have contractual agreements to access the final dataset. Deidentified data are 
submitted to the National Institute of Mental Health Data Archive twice per year, per their requirements. 
 
7.4 Competing interests 
 
AGH, LDS, AMK, DJB, CAC, MD, and LD have received National Institutes of Health funding. AGH 
has received book royalties from Guilford Press and Oxford University Press. Funding 
This study is funded by the National Institute of Mental Health (R01MH120147). The funding agency 
has/had no role in the design, collection, management, analysis, or interpretation of data; the writing of 
the manuscript; or the decision to submit the study protocol for publication. The funding agency has no 
ultimate authority over any of these activities. Additionally, the views expressed are those of the authors 
and do not necessarily represent the views of the Department of Veterans Affairs or any public entity. 
 
7.5 Authors' contributions 
 
AGH, DJB, LD, and AMK conceived of and designed the study and acquired the funding. AGH, ERA, 
MD, KF, JMS, SAH, REH, and CAC are responsible for acquisition of data. LDS drafted the manuscript. 
All authors were involved in revising the manuscript. All authors read and approved the final manuscript. 
 
7.6 Acknowledgements 
 
We are grateful to all the community partners, including the leadership, staff, and patients. 
 
 

25 
 
 
REFERENCES 
 
1.  
Evans S, Banerjee S, Leese M, Huxley P. The impact of mental illness on quality of life: A 
comparison of severe mental illness, common mental disorder and healthy population samples. Qual 
Life Res. 2007 Feb;16(1):17–29. 
2.  
Osborn D, Levy G, Nazareth I, King M. Suicide and severe mental illnesses. Cohort study within the 
UK general practice research database. Schizophr Res. 2008 Feb;99(1–3):134–8. 
3.  
Viron MJ, Stern TA. The impact of serious mental illness on health and healthcare. Psychosomatics. 
2010 Nov;51(6):458–65. 
4.  
Gonzales L, Kois LE, Chen C, López-Aybar L, McCullough B, McLaughlin KJ. Reliability of the 
term “serious mental illness”: A systematic review. Psychiatr Serv. 2022 Nov 1;73(11):1255–62. 
5.  
National Institute of Mental Health. Mental Illness [Internet]. National Institute of Health. 2022 
[cited 2022 Dec 7]. Available from: https://www.nimh.nih.gov/health/statistics/mental-illness 
6.  
Richards DA, Borglin G. Implementation of psychological therapies for anxiety and depression in 
routine practice: two year prospective cohort study. J Affect Disord. 2011 Sep;133(1–2):51–60. 
7.  
Wang PS, Lane M, Olfson M, Pincus HA, Wells KB, Kessler RC. Twelve-Month Use of Mental 
Health Services in the United States [Internet]. Vol. 62, Archives of General Psychiatry. 2005. p. 
629. Available from: http://dx.doi.org/10.1001/archpsyc.62.6.629 
8.  
West JC, Wilk JE, Olfson M, Rae DS, Marcus S, Narrow WE, et al. Patterns and quality of 
treatment for patients with schizophrenia in routine psychiatric practice. Psychiatr Serv. 2005 
Mar;56(3):283–91. 
9.  
Chorpita BF, Regan J. Dissemination of effective mental health treatment procedures: Maximizing 
the return on a significant investment. Behav Res Ther. 2009 Nov;47(11):990–3. 
10.  Williams NJ, Beidas RS. Annual Research Review: The state of implementation science in child 
psychology and psychiatry: a review and suggestions to advance the field. J Child Psychol 
Psychiatry. 2019 Apr;60(4):430–50. 
11.  Castro FG, Barrera M Jr, Martinez CR Jr. The cultural adaptation of prevention interventions: 
resolving tensions between fidelity and fit. Prev Sci. 2004 Mar;5(1):41–5. 
12.  Cohen DJ, Crabtree BF, Etz RS, Balasubramanian BA, Donahue KE, Leviton LC, et al. Fidelity 
versus flexibility: translating evidence-based research into practice. Am J Prev Med. 2008 Nov;35(5 
Suppl):S381-9. 
13.  Rabin BA, Brownson RC. Developing the terminology for dissemination and implementation 
research. In: Dissemination and Implementation Research in HealthTranslating Science to Practice. 
Oxford University Press; 2012. p. 23–52. 
14.  Ritchie MJ, Drummond KL, Smith BN, Sullivan JL, Landes SJ. Development of a qualitative data 
analysis codebook informed by the i-PARIHS framework. Implement Sci Commun. 2022 Sep 
14;3(1):98. 

26 
 
 
15.  Allinder RM, Oats RG. Effects of acceptability on teachers’ implementation of curriculum-based 
measurement and student achievement in mathematics computation. Remedial Spec Educ. 1997 
Mar;18(2):113–20. 
16.  Barnett ML, Brookman-Frazee L, Yu SH, Lind T, Lui JHL, Timmer S, et al. Train-to-Sustain: 
Predictors of Sustainment in a Large-Scale Implementation of Parent-Child Interaction Therapy. 
Evid Based Pract Child Adolesc Ment Health. 2021 Jan 8;6(2):262–76. 
17.  Fenwick K, Henwood B, Lengnick-Hall R, Stefancic A, Gilmer T. Exploring variation in housing 
first implementation: The role of fit. Hum Serv Organ Manag Leadersh Gov. 2019 Oct 
20;43(5):392–406. 
18.  Adashi EY, Geiger HJ, Fine MD. Health care reform and primary care--the growing importance of 
the community health center. N Engl J Med. 2010 Jun 3;362(22):2047–50. 
19.  Sarfan LD, Hilmoe HE, Gumport NB, Harvey AG. The transdiagnostic intervention for sleep and 
circadian dysfunction (TranS-C) in community mental health: Comorbidity and use of modules 
under the microscope. Cogn Behav Pract [Internet]. 2022 Apr; Available from: 
http://dx.doi.org/10.1016/j.cbpra.2022.03.007 
20.  Cook JM, Thompson R, Schnurr PP. Perceived Characteristics of Intervention Scale: Development 
and psychometric properties. Assessment. 2015 Dec;22(6):704–14. 
21.  Marques L, Dixon L, Valentine SE, Borba CPC, Simon NM, Wiltsey Stirman S. Providers’ 
perspectives of factors influencing implementation of evidence-based treatments in a community 
mental health setting: A qualitative investigation of the training–practice gap. Psychol Serv. 2016 
Aug;13(3):322–31. 
22.  Massatti RR, Sweeney HA, Panzano PC, Roth D. The de-adoption of innovative mental health 
practices (IMHP): why organizations choose not to sustain an IMHP. Adm Policy Ment Health. 
2008 Mar;35(1–2):50–65. 
23.  Martin P, Murray LK, Darnell D, Dorsey S. Transdiagnostic treatment approaches for greater public 
health impact: Implementing principles of evidence‐based mental health interventions. Clin Psychol 
(New York). 2018 Dec;25(4):e12270. 
24.  Dalgleish T, Black M, Johnston D, Bevan A. Transdiagnostic approaches to mental health problems: 
Current status and future directions. J Consult Clin Psychol. 2020 Mar;88(3):179–95. 
25.  Fairburn CG, Cooper Z, Shafran R. Cognitive behaviour therapy for eating disorders: a 
“transdiagnostic” theory and treatment. Behav Res Ther. 2003 May;41(5):509–28. 
26.  Harvey A, Watkins E, Mansell W, Shafran R. Cognitive Behavioural Processes across 
Psychological Disorders. London, England: Oxford University Press; 2004. 376 p. 
27.  Baglioni C, Battagliese G, Feige B, Spiegelhalder K, Nissen C, Voderholzer U, et al. Insomnia as a 
predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. J Affect 
Disord. 2011 Dec;135(1–3):10–9. 
28.  Dolsen EA, Asarnow LD, Harvey AG. Insomnia as a transdiagnostic process in psychiatric 
disorders. Curr Psychiatry Rep. 2014 Sep;16(9):471. 

27 
 
 
29.  Hertenstein E, Feige B, Gmeiner T, Kienzler C, Spiegelhalder K, Johann A, et al. Insomnia as a 
predictor of mental disorders: A systematic review and meta-analysis. Sleep Med Rev. 2019 
Feb;43:96–105. 
30.  Kaplan KA, McGlinchey EL, Soehner A, Gershon A, Talbot LS, Eidelman P, et al. Hypersomnia 
subtypes, sleep and relapse in bipolar disorder. Psychol Med. 2015 Jun;45(8):1751–63. 
31.  Kivelä L, Papadopoulos MR, Antypa N. Chronotype and psychiatric disorders. Curr Sleep Med Rep. 
2018 Apr 16;4(2):94–103. 
32.  Vargas I, Muench A, Boyle JT, Gencarelli A, Khader W, Morales K, et al. 0896 the temporal 
dynamics of the association between sleep continuity disturbance and depressive symptoms. Sleep. 
2019 Apr 13;42(Supplement_1):A360–A360. 
33.  Harvey AG, Murray G, Chandler RA, Soehner A. Sleep disturbance as transdiagnostic: 
consideration of neurobiological mechanisms. Clin Psychol Rev. 2011 Mar;31(2):225–35. 
34.  Palagini L, Moretto U, Dell’Osso L, Carney C. Sleep-related cognitive processes, arousal, and 
emotion dysregulation in insomnia disorder: the role of insomnia-specific rumination. Sleep Med. 
2017 Feb;30:97–104. 
35.  Harvey AG, Dong L, Hein K, Yu SH, Martinez AJ, Gumport NB, et al. A randomized controlled 
trial of the Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) to improve 
serious mental illness outcomes in a community setting. J Consult Clin Psychol. 2021 
Jun;89(6):537–50. 
36.  Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better 
mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev. 2021 
Dec;60(101556):101556. 
37.  Taylor DJ, Pruiksma KE. Cognitive and behavioural therapy for insomnia (CBT-I) in psychiatric 
populations: a systematic review. Int Rev Psychiatry. 2014 Apr;26(2):205–13. 
38.  Sarfan LD, Hilmoe HE, Gumport NB, Gasperetti CE, Zieve GG, Harvey AG. Outcomes of the 
Transdiagnostic Intervention for Sleep and Circadian Dysfunction (TranS-C) in a community 
setting: Unpacking comorbidity. Behav Res Ther. 2021 Oct;145(103948):103948. 
39.  Puffer ES, Ayuku D. A community-embedded implementation model for mental-health 
interventions: Reaching the hardest to reach. Perspect Psychol Sci. 2022 Sep;17(5):1276–90. 
40.  Harvey AG, Buysse DJ. Treating sleep problems. New York, NY: Guilford Publications; 2017. 192 
p. 
41.  Buysse DJ. Sleep health: can we define it? Does it matter? Sleep. 2014 Jan 1;37(1):9–17. 
42.  Onken LS, Carroll KM, Shoham V, Cuthbert BN, Riddle M. Reenvisioning clinical science: 
Unifying the discipline to improve the public health. Clin Psychol Sci. 2014 Jan 1;2(1):22–34. 
43.  Cheung JMY, Jarrin DC, Ballot O, Bharwani AA, Morin CM. A systematic review of cognitive 
behavioral therapy for insomnia implemented in primary care and community settings. Sleep Med 
Rev. 2019 Apr;44:23–36. 

28 
 
 
44.  Espie CA, MacMahon KMA, Kelly H-L, Broomfield NM, Douglas NJ, Engleman HM, et al. 
Randomized clinical effectiveness trial of nurse-administered small-group cognitive behavior 
therapy for persistent insomnia in general practice. Sleep. 2007 May;30(5):574–84. 
45.  Karlin BE, Trockel M, Taylor CB, Gimeno J, Manber R. National dissemination of cognitive 
behavioral therapy for insomnia in veterans: therapist- and patient-level outcomes. J Consult Clin 
Psychol. 2013 Oct;81(5):912–7. 
46.  Gumport NB, Yu SH, Harvey AG. Implementing a transdiagnostic sleep and circadian intervention 
in a community mental health setting: A qualitative process evaluation with community 
stakeholders. Psychiatry Res. 2020 Nov;293(113443):113443. 
47.  Kilbourne AM, Neumann MS, Pincus HA, Bauer MS, Stall R. Implementing evidence-based 
interventions in health care: application of the replicating effective programs framework. Implement 
Sci. 2007 Dec 9;2(1):42. 
48.  Curran GM, Bauer M, Mittman B, Pyne JM, Stetler C. Effectiveness-implementation hybrid 
designs. Med Care. 2012 Mar;50(3):217–26. 
49.  Landes SJ, McBain SA, Curran GM. An introduction to effectiveness-implementation hybrid 
designs. Psychiatry Res. 2019 Oct;280(112513):112513. 
50.  Armstrong CC, Dong L, Harvey AG. Mediators and moderators of outcome from the 
Transdiagnostic Sleep and Circadian Intervention for adults with severe mental illness in a 
community setting. Behav Res Ther. 2022 Apr;151(104053):104053. 
51.  Waters F, Chiu VW, Dragovic M, Ree M. Different patterns of treatment response to Cognitive-
Behavioural Therapy for Insomnia (CBT-I) in psychosis. Schizophr Res. 2020 Jul;221:57–62. 
52.  Chan A-W, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 
explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013 Jan 8;346(jan08 
15):e7586. 
53.  Kauth MR, Sullivan G, Blevins D, Cully JA, Landes RD, Said Q, et al. Employing external 
facilitation to implement cognitive behavioral therapy in VA clinics: a pilot study. Implement Sci. 
2010 Oct 13;5(1):75. 
54.  Kilbourne AM, Goodrich DE, Nord KM, Van Poppelen C, Kyle J, Bauer MS, et al. Long-term 
clinical outcomes from a randomized controlled trial of two implementation strategies to promote 
collaborative care attendance in community practices. Adm Policy Ment Health. 2015 
Sep;42(5):642–53. 
55.  Kirchner JE, Ritchie MJ, Pitcock JA, Parker LE, Curran GM, Fortney JC. Outcomes of a partnered 
facilitation strategy to implement primary care-mental health. J Gen Intern Med. 2014 Dec;29 Suppl 
4(S4):904–12. 
56.  Perry CK, Damschroder LJ, Hemler JR, Woodson TT, Ono SS, Cohen DJ. Specifying and 
comparing implementation strategies across seven large implementation interventions: a practical 
application of theory. Implement Sci. 2019 Mar 21;14(1):32. 
57.  Harvey G, Kitson A. Implementing Evidence-Based Practice In Healthcare. Harvey G, Kitson A, 

29 
 
 
editors. London, England: Routledge; 2015. 
58.  Ritchie MJ, Dollar KM, Miller CJ, Smith JL, Oliver KA, Kim B, et al. Using implementation 
facilitation to improve healthcare [Internet]. 2020. Available from: 
https://www.queri.research.va.gov/tools/implementation/Facilitation-Manual.pdf 
59.  Stetler CB, Legro MW, Rycroft-Malone J, Bowman C, Curran G, Guihan M, et al. Role of “external 
facilitation” in implementation of research findings: a qualitative evaluation of facilitation 
experiences in the Veterans Health Administration. Implement Sci. 2006 Oct 18;1:23. 
60.  Ritchie MJ, Dollar KM, Kearney LK, Kirchner JE. Research and services partnerships: Responding 
to needs of clinical operations partners: transferring implementation facilitation knowledge and 
skills. Psychiatr Serv. 2014 Feb 1;65(2):141–3. 
61.  Buysse DJ, Yu L, Moul DE, Germain A, Stover A, Dodds NE, et al. Development and validation of 
patient-reported outcome measures for sleep disturbance and sleep-related impairments. Sleep. 2010 
Jun;33(6):781–92. 
62.  Yu L, Buysse DJ, Germain A, Moul DE, Stover A, Dodds NE, et al. Development of short forms 
from the PROMISTM sleep disturbance and Sleep-Related Impairment item banks. Behav Sleep 
Med. 2011 Dec 28;10(1):6–24. 
63.  Edinger J, Kirby A, Lineberger M, Loiselle M, Wohlgemuth W, Means M. The Duke structured 
interview for sleep disorders. 2004. 
64.  Bootzin RR. Effects of self-control procedures for insomnia. American Journal of Clinical 
Biofeedback. 1979;2:70–7. 
65.  Spielman AJ, Caruso LS, Glovinsky PB. A behavioral perspective on insomnia treatment. Psychiatr 
Clin North Am. 1987 Dec;10(4):541–53. 
66.  Escoffery C, Lebow-Skelley E, Udelson H, Böing EA, Wood R, Fernandez ME, et al. A scoping 
study of frameworks for adapting public health evidence-based interventions. Transl Behav Med. 
2019 Jan 1;9(1):1–10. 
67.  Harvey AG, Lammers HS, Dolsen MR, Mullin AC, Hilmoe HE, Tran M, et al. Systematic review to 
examine the methods used to adapt evidence-based psychological treatments for adults diagnosed 
with a mental illness. Evid Based Ment Health. 2021 Feb;24(1):33–40. 
68.  Gumport NB, Dolsen MR, Harvey AG. Usefulness and utilization of treatment elements from the 
Transdiagnostic Sleep and Circadian Intervention for adolescents with an evening circadian 
preference. Behav Res Ther. 2019 Dec;123(103504):103504. 
69.  Lee SJ, Altschul I, Mowbray CT. Using planned adaptation to implement evidence-based programs 
with new populations. Am J Community Psychol. 2008 Jun;41(3–4):290–303. 
70.  Aarons GA, Green AE, Palinkas LA, Self-Brown S, Whitaker DJ, Lutzker JR, et al. Dynamic 
adaptation process to implement an evidence-based child maltreatment intervention. Implement Sci. 
2012 Apr 18;7(1):32. 
71.  Weisz JR. Building robust psychotherapies for children and adolescents. Perspect Psychol Sci. 2014 

30 
 
 
Jan;9(1):81–4. 
72.  Craske MG, Stein MB, Sullivan G, Sherbourne C, Bystritsky A, Rose RD, et al. Disorder-specific 
impact of coordinated anxiety learning and management treatment for anxiety disorders in primary 
care. Arch Gen Psychiatry. 2011 Apr;68(4):378–88. 
73.  Daumit GL, Dickerson FB, Wang N-Y, Dalcin A, Jerome GJ, Anderson CAM, et al. A behavioral 
weight-loss intervention in persons with serious mental illness. N Engl J Med. 2013 Apr 
25;368(17):1594–602. 
74.  Weisz JR, Chorpita BF, Palinkas LA, Schoenwald SK, Miranda J, Bearman SK, et al. Testing 
standard and modular designs for psychotherapy treating depression, anxiety, and conduct problems 
in youth: a randomized effectiveness trial. Arch Gen Psychiatry. 2012 Mar;69(3):274–82. 
75.  Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, et al. Psychometric assessment 
of three newly developed implementation outcome measures. Implement Sci [Internet]. 2017 
Dec;12(1). Available from: http://dx.doi.org/10.1186/s13012-017-0635-3 
76.  Sheehan DV, Harnett-Sheehan K, Raj BA. The measurement of disability. Int Clin 
Psychopharmacol. 1996 Jun;11(Supplement 3):89–95. 
77.  Leon AC, Olfson M, Portera L, Farber L, Sheehan DV. Assessing psychiatric impairment in primary 
care with the Sheehan Disability Scale. Int J Psychiatry Med. 1997;27(2):93–105. 
78.  Dong L, Martinez AJ, Buysse DJ, Harvey AG. A composite measure of sleep health predicts 
concurrent mental and physical health outcomes in adolescents prone to eveningness. Sleep Health. 
2019 Apr;5(2):166–74. 
79.  Clarke DE, Kuhl EA. DSM-5 cross-cutting symptom measures: a step towards the future of 
psychiatric care? World Psychiatry. 2014 Oct;13(3):314–6. 
80.  Narrow WE, Clarke DE, Kuramoto SJ, Kraemer HC, Kupfer DJ, Greiner L, et al. DSM-5 field trials 
in the United States and Canada, Part III: development and reliability testing of a cross-cutting 
symptom assessment for DSM-5. Am J Psychiatry. 2013 Jan;170(1):71–82. 
81.  Hamilton CM, Strader LC, Pratt JG, Maiese D, Hendershot T, Kwok RK, et al. The PhenX Toolkit: 
get the most from your measures. Am J Epidemiol. 2011 Aug 1;174(3):253–60. 
82.  Devilly GJ, Borkovec TD. Psychometric properties of the credibility/expectancy questionnaire. J 
Behav Ther Exp Psychiatry. 2000 Jun;31(2):73–86. 
83.  Harrow M, Grossman LS, Herbener ES, Davies EW. Ten-year outcome: patients with 
schizoaffective disorders, schizophrenia, affective disorders and mood-incongruent psychotic 
symptoms. Br J Psychiatry. 2000 Nov;177(5):421–6. 
84.  Large M, Mullin K, Gupta P, Harris A, Nielssen O. Systematic meta-analysis of outcomes 
associated with psychosis and co-morbid substance use. Aust N Z J Psychiatry. 2014 
May;48(5):418–32. 
85.  Raudenbush SW. Optimal design software for multi-level and longitudinal research [Software] 
[Internet]. 2011. Available from: www.wtgrantfoundation.org 

31 
 
 
86.  Spybrook J, Bloom H, Congdon R, Hill C, Martinez A, Raudenbush SW. Optimal design for 
longitudinal and multilevel research: Documentation for the “Optimal Design” software. 2011. 
87.  StataCorp. Stata Statistical Software: Release 15. 2017. 
88.  Chorpita BF, Park A, Tsai K, Korathu-Larson P, Higa-McMillan CK, Nakamura BJ, et al. Balancing 
effectiveness with responsiveness: Therapist satisfaction across different treatment designs in the 
Child STEPs randomized effectiveness trial. J Consult Clin Psychol. 2015 Aug;83(4):709–18. 
89.  Briesch AM, Chafouleas SM, Neugebauer SR, Riley-Tillman TC. Assessing influences on 
intervention implementation: revision of the usage rating profile-intervention. J Sch Psychol. 2013 
Feb;51(1):81–96. 
90.  Kilbourne AM, Abraham KM, Goodrich DE, Bowersox NW, Almirall D, Lai Z, et al. Cluster 
randomized adaptive implementation trial comparing a standard versus enhanced implementation 
intervention to improve uptake of an effective re-engagement program for patients with serious 
mental illness. Implement Sci. 2013 Nov 20;8(1):136. 
91.  Waxmonsky J, Kilbourne AM, Goodrich DE, Nord KM, Lai Z, Laird C, et al. Enhanced fidelity to 
treatment for bipolar disorder: results from a randomized controlled implementation trial. Psychiatr 
Serv. 2014 Jan 1;65(1):81–90. 
92.  Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and 
feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev. 
2012 Jun 13;(6):CD000259. 
93.  Little RJ, Rubin DB. Statistical analysis with missing data. Wiley-Interscience; 2014. 409 p. 
94.  Beach ML, Meier P. Choosing covariates in the analysis of clinical trials. Control Clin Trials. 1989 
Dec;10(4 Suppl):161S-175S. 
95.  Canner PL. Covariate adjustment of treatment effects in clinical trials. Control Clin Trials. 1991 
Jun;12(3):359–66. 
96.  Senn SJ. Covariate imbalance and random allocation in clinical trials. Stat Med. 1989 Apr;8(4):467–
75. 
97.  Wysocki AC, Lawson KM, Rhemtulla M. Statistical control requires causal justification. Adv 
Methods Pract Psychol Sci. 2022 Apr;5(2):251524592210958. 
98.  Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach 
to multiple testing. J R Stat Soc. 1995 Jan;57(1):289–300. 
99.  Moyé LA. Disciplined analyses in clinical trials: the dark heart of the matter. Stat Methods Med 
Res. 2008 Jun;17(3):253–64. 
100.  Goldstein H. Multilevel statistical models. Chichester, UK: John Wiley & Sons, Ltd; 2010. 
101.  Hox JJ, Roberts JK, editors. Handbook of advanced multilevel analysis. Routledge; 2011. (European 
Association of Methodology). 
102.  Raudenbush SW, Bryk AS. Hierarchical linear models. 2nd ed. Thousand Oaks, CA: SAGE 

32 
 
 
Publications; 2002. 512 p. (Advanced Quantitative Techniques in the Social Sciences). 
103.  Preacher KJ, Zyphur MJ, Zhang Z. A general multilevel SEM framework for assessing multilevel 
mediation. Psychol Methods. 2010 Sep;15(3):209–33. 
104.  Szafranski DD, Smith BN, Gros DF, Resick PA. High rates of PTSD treatment dropout: A possible 
red herring? J Anxiety Disord. 2017 Apr;47:91–8. 
105.  Brabson LA, Harris JL, Lindhiem O, Herschell AD. Workforce turnover in community behavioral 
health agencies in the USA: A systematic review with recommendations. Clin Child Fam Psychol 
Rev. 2020 Sep;23(3):297–315. 
106.  Reneses B, Muñoz E, López-Ibor JJ. Factors predicting drop-out in community mental health 
centres. World Psychiatry. 2009 Oct;8(3):173–7. 
107.  Jacobson NS, Truax P. Clinical significance: A statistical approach to defining meaningful change in 
psychotherapy research. J Consult Clin Psychol. 1991;59(1):12–9. 
108.  Lewis CC, Klasnja P, Powell BJ, Lyon AR, Tuzzio L, Jones S, et al. From classification to 
causality: Advancing understanding of mechanisms of change in implementation science. Front 
Public Health [Internet]. 2018 May 7;6. Available from: http://dx.doi.org/10.3389/fpubh.2018.00136 
109.  Reichenpfader U, Carlfjord S, Nilsen P. Leadership in evidence-based practice: a systematic review. 
Leadersh Health Serv (Bradf Engl). 2015;28(4):298–316. 
110.  Kopelovich SL, Monroe-DeVita M, Buck BE, Brenner C, Moser L, Jarskog LF, et al. Community 
mental health care delivery during the COVID-19 pandemic: Practical strategies for improving care 
for people with serious mental illness. Community Ment Health J. 2021 Apr;57(3):405–15.