Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention Practices in Early Care and Education Settings
Phase: NA
Conditions: Obesity, Childhood, Nutrition Aspect of Cancer
Sponsor: University of Arkansas

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 1 
 
 
 
 
Study Title: Testing an Adaptive Implementation Strategy to Optimize Delivery of 
Obesity Prevention Practices in Early Care and Education Settings 
 
 
 
Principal Investigator:  
Taren Swindle, PhD 
University of Arkansas for Medical Sciences 
4301 W. Markham Street, Slot # 531 
Little Rock, AR 72205 
Telephone:  870-236-0997 
Email:  tswindle@uams.edu 
 
 
Sub-Investigator (s): 
Geoffrey Curran, PhD 
 
University of Arkansas for Medical Sciences 
Telephone:  (501) 686-7610 
Email:  CurranGeoffreyM@uams.edu 
 
 
Jacob Painter, PhD 
 
University of Arkansas for Medical Sciences 
Telephone:  (501) 686-7123 
Email:  JTPainter@uams.edu 
 
 
Leanne Whiteside-Mansell, EdD 
 
University of Arkansas for Medical Sciences 
Telephone:  (501) 686-7633 
Email:  WhitesideMansellLeanne@uams.edu 
 
 
James Selig, PhD 
 
University of Arkansas for Medical Sciences 
Telephone:  (501) 526-6723 
Email:  JPSelig@uams.edu 
 
 
Julie Rutledge, PhD 
 
Louisiana Tech University 
 
P.O. Box 3167 
 
Ruston, LA 71272 
Telephone:  318-257-4456 
Email:  rutledge@latech.edu 
 
 
Susan Johnson, PhD 
 
University of Colorado Anschutz Medical Campus 
12631 East 17th Avenue, Mail Stop F-561 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 2 
 
Academic Office Building, Room 2609 
Aurora, CO 80045 
Telephone:  303-724-2923 
Email:  susan.johnson@cuanschutz.edu 
 
 
Study locations: 
University of Arkansas for Medical Sciences  
 
Louisiana Tech University 
 
Funding:  
NIH NCI 1R37CA252113 
 
 
 
 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 3 
 
Background and Rationale 
By the late 1960s, researchers established that most cancers were related to 
environmental factors.1 Fewer than 10 years later, studies showed that diet was just as 
much a cause of cancer as smoking.2 Further, excess weight is linked with higher risk of 
13 cancers,3 and the US has the highest rate of cancer attributable to body mass index 
(BMI).4 Over 600,000 individuals die, and $80.2 billion are spent each year as a result of 
cancer in the US.5 The National Cancer Institute recommends healthy eating and weight 
management for cancer prevention.6 However, Arkansas (AR) and Louisiana (LA) are 
among the states in the US with the highest obesity rates, lowest quality diets, and 
highest cancer rates.7 Given the limited economic resources of these states, community 
systems need obesity prevention efforts that optimize resources through innovative, 
tailored implementation. Disparate disease burden and high healthcare costs will persist 
without effective implementation of evidence-based interventions.  
Dietary habits and weight trajectories in early life predict later health outcomes;8,9 
thus, obesity prevention efforts must target young children. Specifically, children are 5 
times more likely to remain overweight or obese in adulthood if they are overweight in 
preschool.10 On average, 60% of US children under age 5 (15 million children) have at 
least 1 non-parental childcare arrangement per week.11 Children spend 36 hours a 
week in ECE settings, on average.12 Thus, the early care and education (ECE) 
environment is prime for reaching young children for obesity prevention. Despite the 
potential for ECE to promote healthy habits, a gap exists between current practices and 
evidence-based practices (EBPs).13 This gap indicates a need to “overcome barriers to 
the adoption, adaptation, integration, and scale-up (PA-19-274)” of research evidence in 
ECE. Our long-term goal is to increase EBP implementation in ECE to improve child 
diet and health outcomes.  
Consistent with World Cancer Research Fund (WCRF) recommendations,14 
Together, We Inspire Smart Eating (WISE) aims to increase children’s intake of 
carotenoid-rich fruits and vegetables (FV). WISE was co-developed with end users to 
meet the curricular and budgetary needs of the ECE context15,16 and is included in the 
US Department of Agriculture SNAP-Ed toolkit.17 Research supports each WISE EBP: 
(1) multiple hands-on exposures to FV support food acceptance;18–24 (2) role modeling 
by educators allows children to observe a trusted adult eating FV; 25–27 (3) positive 
feeding practices support children’s self-regulation;27–29 and (4) mascot use associates 
a familiar character with FV.30–35 Each EBP aligns with the Academy of Nutrition and 
Dietetics’ “Benchmarks for Nutrition in Childcare.”36 Evidence also supports WISE as a 
whole.37,38 Compared to usual education, WISE increased FV intake37 (8% increase in 
healthy carotenoid levels; 4% decrease in unhealthy range).38 Also consistent with 
WCRF guidance,14 parents reported significantly decreased fast food and sugar-
sweetened beverages intake after a year of WISE.37,39 Thus, WISE has a positive 
impact in areas related to adult cancer risk. 
Standard approaches to WISE implementation (training and reminders only) have 
resulted in challenges and suboptimal fidelity to EBPs.40 Little research exists to guide 
solutions. For example, although studies have demonstrated that implementation 
strategies can promote policy implementation (e.g., menu offerings) and improve the 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 4 
 
environment (e.g., access to water),41 few studies have assisted educators to implement 
EBPs in ECE.42 Further, no available studies report on implementation mechanisms in 
ECE42,43 (how and why strategies work for whom) or on cost-effectiveness of 
implementation strategies in ECE. Thus, practitioners lack data to drive decisions on 
EBP implementation in ECE.  
Theoretical Foundation. The integrated Promoting Action on Research 
Implementation in Health Services (i-PARIHS) framework posits that components of 
successful implementation include characteristics of the innovation (the EBPs), 
recipients, context, and facilitation (i.e., implementation support).44 Successful 
implementation takes place when facilitation promotes the acceptance and use of an 
innovation based on the recipients’ and context’s needs. Facilitation exists along a 
continuum.45 On one end, task-focused support provides technical and practical help. 
On the other end, holistic facilitation provides enabling support to cultivate shared 
meaning, connected networks, and personal development.45 A central tenant of i-
PARIHS is that successful implementation requires different levels and kinds of 
facilitation depending on characteristics of the innovation, the context, and recipients. 
The i-PARIHS framework guides our proposal in several ways. Our formative work 
(K01-DK110141) identified determinants of WISE EBP implementation by applying i-
PARIHS. These determinants guided engagement with stakeholders to select and tailor 
the proposed implementation strategies. Stakeholders prioritized facilitation as a key 
strategy to improve WISE EBP implementation, and we will tailor facilitation to reflect 
recipient and contextual needs. While i-PARIHS is ideal to inform implementation 
strategy tailoring, research has not tested it in this way (our Aim 1). Further, i-PARIHS 
has received limited tests of underlying mechanisms (our Aim 2),46–48  
with most studies in health care.49,50  
Consistent with i-PARIHS, adaptive implementation strategies reflect that a one-
size-fits-all approach may not serve all settings well. 51 Not all sites may need all 
strategies; giving sites more than they need is expensive and wasteful. An adaptive 
implementation strategy provides decision points and tailoring variables to optimize 
resources. Table 1 presents the 
design features of an adaptive 
implementation strategy. In sum, 
an adaptive implementation 
strategy provides a “replicable 
guide” for who gets what 
implementation support and 
when.52  
Hypothesis and/or Specific 
Aims or Objectives 
Our proposed project will determine the optimal implementation intensity needed to 
improve the uptake of WISE EBPs in ECE. The overall objectives of this project are to 
determine the effectiveness and cost-effectiveness of an adaptive implementation 
approach to improve adoption of the WISE EBPs while also examining moderators and 
mediators of response to the strategies. Our central hypothesis is that the addition of 
Table 1. Adaptive Implementation Strategies Decision 
Rules 
Design Features 
Definition 
Crucial decision 
points 
Which strategies to begin the study  
(i.e., low intensity) 
How and when response is measured 
What strategies are given to non-
responders (i.e., high intensity) 
Tailoring variables 
Measurement to identify non-
responders and inform strategy 
intensity 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 5 
 
high-intensity strategies at sites that do not respond to low-intensity strategies will 
improve implementation and health outcomes compared to the continuation of low-
intensity strategies. Our long-term goal is to increase EBP implementation in the ECE 
setting to improve health outcomes for children. Thus, we propose the following specific 
aims. 
 
Specific Aim 1. Determine the effectiveness of an adaptive implementation 
strategy that tailors the intensity of implementation support versus a low-
intensity strategy.   
Specific Aim 2. Examine moderators and mediators of implementation outcomes 
in a mixed-methods design.   
Specific Aim 3. Assess the incremental cost-effectiveness of the adaptive 
implementation strategy.  
Study Design and Procedures 
We will use an enhanced non-responder trial51 design to determine the effectiveness 
(Aim 1) and incremental cost-effectiveness (Aim 3) of an adaptive implementation 
strategy for WISE, while examining moderators and mediators of the strategy effect 
(Aim 2). In this trial, we will randomize sites that do not respond to low-intensity 
strategies to either (a) continue receiving low-intensity strategies or (b) receive high-
intensity strategies (See Figure 1). This design will determine the effect of an adaptive 
implementation strategy that 
adds high intensity versus 
one that continues with low 
intensity among non-
responder sites. This trial 
design has two key 
advantages. 51 First, data 
collection across the entire 
study informs identification of 
moderators to inform 
improvements to the adaptive 
implementation strategy 
guide (i.e., who needs what 
and when). Second, 
secondary analyses can consider the time course of response (i.e., whether some sites 
need more time with low intensity, if early responders maintain response).  
Study Population 
Sites will be from 4 geographic regions: Central AR, AR River Valley, North Central 
LA, and Southeast LA.  A site is one ECE location; a site may have multiple classrooms 
with up to 20 children per classroom. A director provides leadership at each site; 
educators implement WISE in their classrooms.  
Figure 1. Cluster-Randomized Enhanced Non-Responder Trial 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
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Site Inclusion/Exclusion Criteria: Sites within a 100-mile radius of staff offices; 
participating in the Child and Adult Care Food Program (CACFP) and the state’s quality 
rating system; serving 15+ children ages 3 to 5 years; agreeing to participate in data 
collection; and not currently using WISE can be included.  We will exclude sites 
unwilling to adopt WISE for all classrooms. Focusing on CACFP will maximize equitable 
reach, generalizability, and study impact. A focus on CACFP builds upon our prior work 
that documented gaps in adoption and significant barriers to EBP use in CACFP-funded 
sites (i.e., all USDA/K01 sites were CACFP).53 While CACFP provides sound meal 
pattern guidance, this guidance has neither reliably improved educators’ knowledge or 
practice54 nor children’s healthy food intake.54 Further, studies demonstrate 
implementation of the most recent CACFP standards at fewer than 23% of CACFP sites 
in some states.55 Most importantly, CACFP is a federal system that serves 3 million 
children per year,56 “targeting benefits to those children most in need.”57 CACFP 
provides snack funds, removing the barrier of food costs for WISE implementation. 
Classroom Inclusion Criteria: All classrooms at a site will receive the same 
implementation strategies and participate in data collection. This reflects stakeholder 
input that sites would not participate unless all classrooms are treated equally. For 
analyses, we will include only classrooms that are non-responders. In this way, we 
avoid contaminating analyses with classrooms that respond to the low-intensity strategy 
against the site trend. Based on our ECE experience, we expect 3 non-responding 
classrooms per site, on average; we have powered accordingly. Thus, we plan to 
include 192 total classrooms at 64 sites in our primary analyses. The early responder 
sites and classrooms (16 sites, 48 classrooms) will be included in exploratory analyses.  
Child Inclusion Criteria: We will select one classroom at random per site to 
participate in collection of child outcomes (N = 64 classrooms, 15 children per 
classroom = 960 children total). Specifically, all classrooms at the site will be assigned a 
number (e.g., 1-10 at a site with 10 classrooms). Next, the biostatistician will use a 
random number generator to select a number. The classroom corresponding to that 
number will be targeted for child participation.  Children will be between 3 and 5 years of 
age.  
Relevance of Results to the Community. Community partnership is key to reduce 
cancer-related health disparities.122,123 To this end, we will draw on Evidence-Based 
Quality Improvement (EBQI) methods, in which we are well-versed. 81,124,125 This 
process will develop researcher–stakeholder partnerships for joint decision 
making,81,124–129 consistent with Community-Engaged Dissemination and 
Implementation principles.130 Our EBQI panel will include educators, directors, and staff 
from ECE as well as state policy leaders who can inform WISE scale-up. For example, 
at least 3 professional networks could use the adaptive approach we develop: CACFP 
sponsors, Childcare Resource and Referral (CCR&R) agents, and USDA Cooperative 
Extension agents (See LOS). The panel will meet 3 times per year and provide input 
into study recruitment, roll out of study protocol, interpretation of findings, and future 
planning. We will also disseminate our results back to participants and stakeholders 
through infographic-style summaries and presentations at community events. We will 
pay stakeholders $50 for participation in these meetings. 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 7 
 
 
Specific Aim 1. Determine the effectiveness of an adaptive implementation 
strategy that tailors the intensity of implementation support versus a low-
intensity strategy.  
Table 3 presents the Aim 1 
data collection plan. Measures 
align with Proctor’s Outcomes 
for Implementation Research 
taxonomy.58 The school year 
calendar informs measurement 
timing. The primary outcome 
is fidelity to the WISE EBPs 
at the classroom level. We 
will use the WISE fidelity 
observational measure.64 The 
measure includes 2 to 3 items 
per EBP on a 1 to 4 scale to 
receive an average, continuous fidelity score with 4 representing the highest fidelity. For 
each item, values are anchored to concrete, observable behaviors. Trained and field-
reliable staff blinded to the study condition will collect fidelity data consistent with 
published protocols.64  
Secondary implementation outcomes are adoption as well as acceptability, feasibility, 
and appropriateness of WISE and the implementation strategies.59 We will collect 
secondary outcomes through self-report from educators on the schedule in Table 2. 
The WISE delivery survey40 captures the number/content of lessons delivered and 
material dissemination to parents. In the next school year, we will assess EBP 
sustainment (i.e., delivery and fidelity 12-18 months after initial implementation).  All  
measures will be submitted to the IRB for approval before use in the study.  
To measure the effect on child health outcomes, we will use Resonance Raman 
Spectroscopy (RRS), which measures skin carotenoid levels as a biomarker for colorful 
FV intake61 with an optical hand scan.62,63 RRS reflects intake over the prior 4 weeks 
and is sensitive to individual differences and experimental changes.64,65 Trained staff 
will assess BMI with a standardized protocol60 and interpret the data with 2000 CDC 
growth charts.66 Finally, we will observe children’s target food intake with a standardized 
protocol used by our team in prior studies.127–128 We will weigh food portions (to the 
nearest 0.1 g) before and after observation.   
Implementation Processes. First, site leadership will meet with WISE facilitators to 
discuss the formal commitment and implementation blueprint. Next, all staff will receive 
WISE training.  At training, educators will receive the “reminder cutting board,” showing 
the 4 WISE EBPs for use during lessons. Next, sites will select a “champion” to be a 
liaison between the site and WISE facilitator. Champions receive standardized training 
to navigate WISE implementation before September.  
Facilitation. In the low intensity group, WISE facilitators will provide monthly task-
focused facilitation targeted to site directors and champions. Facilitation in the low-
Table 2.  Aim 1 Measures & Data Collection Plan  
Constructs 
Measures 
Frequency 
Fidelity 
WISE fidelity64 
Oct, Jan, April of 
school year 
Acceptability, feasibility, 
appropriateness of 
innovation and 
strategies 
Weiner et al. 
pragmatic 
measures59  
Aug, Jan, April of 
school year 
Adoption 
WISE delivery 
survey40 
Monthly during 
implementation 
year 
Sustainability 
WISE delivery, 
WISE fidelity64 
Fall of following 
school year 
Child health outcomes 
RRS,130 BMI,60 
consumption12
7 
Aug & April of 
school year 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 8 
 
intensity group will monitor implementation, identify and solve problems related to 
contextual barriers, and assist with navigating structural changes needed for WISE. In 
the high intensity group, WISE facilitators will provide holistic, enabling facilitation 
tailored to the needs of the educators twice per month and more upon request. 
Facilitation in the high-intensity group will support educators in a one-on-one fashion, 
helping to set goals, fostering peer networking, developing shared vision among leaders 
and staff, and building meaningful relationships that support change efficacy. This will 
include the provision of tailored educational materials and coaching based on observed 
fidelity reports. Each study region will have 2 trained facilitators with experience in the 
ECE setting and/or WISE.  Further, all facilitators will receive standard training and 
toolkits (e.g., sample scripts, testimonials, motivational interview examples). This is 
based on the Veterans Health Administration Implementation Facilitation Training,70 
which 2 study staff completed in 2019. This training has been adapted for WISE and 
delivery in a 4-hour session. After training, new facilitators will accompany experienced 
facilitators for 2 field visits to observe. The new facilitators will lead at least 2 visits with 
support and feedback from the experienced facilitator. Facilitators will take part in 
monthly reflective supervision calls led by the PI. All facilitators will log their activities 
(e.g., visits, calls, emails, champion contacts). The PI will compare the facilitator logs 
against the core implementation facilitation activities checklist for fidelity monitoring 71 
and provide corrective guidance as needed. 
Specific Aim 2. Examine moderators and mediators of implementation outcomes 
in a mixed-methods design.  
We will use an explanatory, sequential mixed methods design (QUANT→qual) to 
provide a nuanced understanding of implementation mechanisms and contextual 
factors.72,73 Quantitative analyses will test 2 moderators and 3 mediators specified a 
priori (See Figure 2), while qualitative methods will provide detail and elaborate on 
potential additional candidate moderators and mediators at a subset of sites of 
purposively selected sties based on response type.   
Quantitative Procedures. During the baseline period (prior to Oct), educators at 
participating sites will complete assessments of potential moderators and mediators. 
Educators will also complete surveys mid-year and at the school year end to assess 
proposed mediators. We expect 2 educators per classroom to complete the survey, 6 
per site on average. This follows the best practice of assessing moderators before 
randomization74,75 and assessing mediators at a minimum of three points in time.76,77 
Further, our design includes key features to establish causal inference including 
temporality and experimental manipulation of dosages of facilitation.78 Educator 
responses will reflect site experiences, and we will aggregate educator responses to the 
site level for analyses. Teachers will complete surveys at baseline, mid-point, and follow 
up. We will pay them $15 per survey ($45 total).  The research team will collect these 
data in person with paper surveys or emailed survey links (reflecting technology access 
and use in ECE). We will capture data with REDCap for secure storage. All classroom 
staff (lead and assistants) will complete assessments; assessments will be submitted 
for IRB approval prior to use in the study. These measures will be compiled and 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 9 
 
submitted for approval in the first 6 months of the study while site recruitment efforts are 
underway.  
Moderation. Organizational culture predicts care quality in health care79,80 and 
moderates response to implementation strategies.81 Further, research by our team and 
others has linked early educator background with personal nutrition and EBP use,82–85 
suggesting it as a prime target for moderation of response. 
Moderation Measures. Moderation measures will include The Organizational 
Readiness to Change Assessment (ORCA),86 which we adapted and tested in ECE in 
our prior work. For this study, we will focus on the context subscale, which is consistent 
with our focal moderator and based on i-PARIHS. The baseline survey will also capture 
educator background including years of experience and type/ frequency of prior nutrition 
and feeding training to create a composite educator experience variable.  
Mediation. Lewis et al.87 recommend a process for examining mediators in 
implementation research: (1) specify implementation strategies, (2) generate strategy–-
mechanism linkages, and (3) identify proximal and distal outcomes. For both the low- 
and high-intensity strategies, implementation facilitation provides the umbrella under 
which other implementation strategies are used. Thus, we will focus on mechanisms 
theorized to be activated by facilitation. First, we hypothesize that facilitation will 
decrease the perceived barriers to implementation (e.g., by helping to identify problems 
and solutions).45 The presence of fewer perceived barriers has been associated with 
improved EBP use.88 Second, we expect that facilitation will improve implementation 
climate (e.g., by developing shared meaning,45 assisting with boundary navigation, and 
supporting role clarity45,89). Implementation climate has been associated with positive 
attitudes towards EBPs,90 success in medication management implementation,91 and 
improvements in evidence-based psychotherapy use.92 Finally, we hypothesize that 
facilitation will improve implementation leadership and the knowledge and behaviors 
leaders leverage to support EBP implementation93 (e.g., by navigating group interests, 
modeling empowerment, and building organizational structures45,89). Implementation 
leadership has been associated with positive attitudes toward EBPs,90 success in 
medication management implementation and EBP use in acute care,94 and 
implementation improvement in evidence-based psychotherapies and community 
mental health.95 For steps (2) and (3), Figure 2 presents our proposed model that links 
facilitation to the proposed mediators (proximal outcomes) and targeted implementation 
outcomes (distal outcomes).   
Mediation Measures. Facilitation (e.g., dose, target) will be measured using the 
facilitation logs described in Aim 1; however, we will conceptualize facilitation 
dichotomously for analyses (high and low intensity). We will apply widely used and 
validated measures of proposed mechanisms, including the Implementation Climate 
Scale96 and Implementation Leadership Scale,93 recently adapted for educational 
settings;97 the perceived barriers measure53 is a checklist of challenges educators 
reported in our formative work and used in the K01.  
Qualitative Procedures. We will use qualitative data to provide detailed 
understanding of response to low- and high-intensity strategies. Specifically, 
quantitative data from the enhanced non-responder trial will identify 5 categories of 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 10 
 
response to implementation strategies (Figure 1): (1) early responders to low-intensity 
(by Oct), (2) late 
responders to low-intensity 
(by school year end), (3) 
non-responders to low-
intensity, (4) responders to 
high-intensity, and 5) non-
responders to high 
intensity. Through 
purposive site visits, we will 
collect qualitative data within each response type. We expect to target 3 sites per 
response type to reach a total sample of 15 sites (split across state and study years). 
We expect to reach saturation with 15 sites, but we are prepared to increase to 20 if 
needed to reach saturation. During site visits, the research team will conduct semi-
structured, key informant interviews with directors and focus groups with educators (4 to 
6 educators per Krueger72,98). This format is cost-effective and will allow educators to 
share experiences (independent of directors).72  All participants in focus groups and 
interviews will receive a $25 incentive. 
 Qualitative Measures. Both director interviews and educator focus groups will probe 
to provide an in-depth understanding of our proposed moderators and mediators while 
exploring additional unanticipated ones. Director interviews and educator focus groups 
will elicit perceived reasons why the strategies worked (or failed) at their site, practical 
strategies of leadership  
support, and relevant factors in the implementation climate. Concepts from the i-
PARIHS framework will inform interviews and focus groups guides (Table 4). 
Additionally, the research team will capture field notes of the site activities, processes, 
and interactions that may influence response to the strategies. We will submit interview 
guides for IRB approval prior to collection of qualitative data.  
 
Specific Aim 3.  Assess the incremental cost-effectiveness of the adaptive 
implementation strategy. 
Cost-effectiveness analysis (CEA) constructs an incremental cost-effectiveness ratio 
(ICER) to estimate the marginal difference in costs and effectiveness of 2 
implementation strategies. We will use CEA to estimate the incremental differences 
seen with continuing low-intensity strategies versus augmenting with high-intensity 
strategies on costs and (a) fidelity and (b) child health outcomes. 
Measures. We will calculate implementation strategy costs based on time and travel 
data in the facilitation log and known material purchase costs. Based on work by Ritchie 
et al.,99 facilitators will log all activities and travel time using REDCap, which was tested 
and found feasible in our prior work. 
 
Risks and Benefits 
Table 3. Sample interview questions by i-PARIHS constructs.  
Context 
What is it like to work at this center? How did that 
influence implementing WISE? How did your 
leadership get involved?  
Innovation 
Tell me about how WISE worked in your classroom.  
Recipients 
As you implemented WISE, what was most helpful to 
you? Least helpful?  
Facilitation 
Who was your WISE coach? How did you interact with 
them? What did the WISE coach do that helped? What 
do you wish they had done to better support you? 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
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Minimal risk is anticipated. However, the following possible risks have been identified:  
1. Discomfort: The open-ended interviews and surveys with educators may include 
questions that some teachers may experience discomfort in answering.  
2. Confidentiality: Classroom observations will record behaviors specific to each 
teacher. Disclosure of this information could be damaging to the teachers, both 
personally and professionally.  Further, a risk to study participants is the potential 
for loss of confidentiality of study data.   
3. Parents may prefer that their child not participate in RRS, BMI, and child 
consumption assessments.  
 
Protection Against Risk 
1. Discomfort: Teachers may choose not to answer survey questions or to stop 
interviews at any time. Directors will also be assured they can withdraw participation 
at any time.   We do not expect the questions to elicit a strong emotional response.  
 
2. Confidentiality: Each center will be informed of the private nature of the teacher 
surveys/interviews and classroom observations and that these records will not be 
accessible by their agency. The audio and transcription files will be deidentified and 
stored electronically on a secured server.  Physical copies of the interviews and 
interview notes will be stored in locked filing cabinets in an office suite. Interviews 
and observations will be tracked with neutral identification numbers, never including 
teacher names, addresses, or birthdates.  
3. No child will be included in study activities if a parent has indicated they do not 
want their child to participate.  
There may be some indirect benefit to participants taking part in this study. As early 
care and education programs learn more about effective practices for obesity 
prevention through the findings of this project, they may provide better training 
opportunities and support for teachers. Additionally, educators in prior studies have 
reported WISE training and food experiences as beneficial to their personal health. 
Stakeholders have reported service on EBQI panels as highly rewarding as well. It is 
also possible that participants will experience no direct benefit as a result of 
participation.  However, the potential benefit to others outside the research study is 
great. Development and evaluation of implementation strategies to support obesity 
prevention will provide critical knowledge on the value of investments in adding 
implementation support strategies to existing obesity prevention interventions. 
Specifically, this study will give information about what level of implementation 
support is likely to be beneficial and for whom. Further, we believe that information 
gained will contribute to future uptake and sustainability of environmental 
interventions to improve diet of children. 
The Data Safety and Monitoring board will assess the balance or risks and benefits 
throughout the study period (See Appendix A).  
Data Handling and Recordkeeping 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 12 
 
 
For all quantitative data, we will use REDCap software to securely collect and 
store all survey data. We will also use RedCap software for quantitative assessments at 
site visits because REDCap has offline secure storage capacity. For these 
observations, data collectors will observe teachers in the classroom to complete ratings 
of feeding practices. All quantitative data will be transferred into SPSS, a statistical 
processing software. In-depth interviews and focus groups (Aim 2) will be audio-
recorded and transcribed and imported into Nvivo for analyses. All data will be paired 
with a neutral identification number. This information will only be accessible to the PI 
and research assistant. Names will be removed from all files after the follow-up data 
collection.  
BMI data will be captured using paper data forms and then entered into SPSS. 
Resonance Raman Spectroscopy information is captured by the device computer and 
sent to a secure server. All data will be paired with a neutral identification number. The 
same process will be followed for child consumption observations. Containers will be 
labeled and weights recorded alongside a neutral identification number. A key file 
linking ID numbers with participant names will only be kept between pre and post 
assessments to be able to link participants across time.  This information will only be 
accessible to the PI, LA Site lead, and site Project managers.  
The Principal Investigator will carefully monitor study procedures to protect the safety of 
research subjects, the quality of the data and the integrity of the study.  All study staff 
will be certified in human subjects research. All study subject material Information 
obtained will be summarized without identification.  Participants will have no identifying 
information linked with their responses.  Study documentation will be kept in a locked 
file in the principal investigator’s office, if hardcopy, or on a password-protected server.  
We will destroy identifiers and contact of study participants after final data collection and 
analysis are complete and results of the project shared with participants. We expect this 
to take up to 7 years.   
Multisite Research 
The University of Arkansas for Medical Sciences (UAMS) will serve as the single IRB for 
this study for all participating sites, including Louisiana Tech University and the 
University of Colorado. The UAMS IRB is registered with the federal Office for Human 
Research Protections per 45 CFR 46 Part E. The UAMS IRB has the professional 
competence necessary to review the proposed research.  Louisiana Tech University (LA 
Tech) and the University of Colorado Anschutz Medical Campus have expressed their 
willingness to rely upon the review of a single IRB of Record for this study. The 
Anschutz Medical Campusand UAMS are already participants in the SMART IRB 
agreement (NIH’s National Center for Advancing Translational Sciences [NCATS] 
Streamlined, Multi-site, Accelerated Resource for Trials [SMART] IRB Reliance 
Platform). LA Tech will use the UAMS reliance agreement to cede IRB oversight.  
 
The UAMS PI (Swindle) will: 
 Coordinate communication between sites 
 Request and receive information and documentation from sites 

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 Develop template materials for review by the UAMS IRB and for limited 
modification by relying sites 
 Submit materials from all sites to the UAMS IRB and coordinate responses to 
any IRB queries 
 Provide documentation to relying sites 
 
Relying sites will follow local procedures to coordinate, collect, and verify information 
such as: 
 Site variations in areas such as recruiting, informed consent, HIPAA, populations 
 Conflict of interest disclosure and management 
 Completion of ancillary reviews 
 Training and qualifications of study team 
 Continuing review or closure information 
 Reportable events 
 
Relying sites will provide necessary information or assurances to the UAMS study team 
for submission to the UAMS IRB. The UAMS IRB office will communicate directly with 
the UAMS study team as the proxy for all relying sites. Relying sites will follow their 
local procedures for dissemination of information and documentation.  
 
UAMS will maintain records of the authorization/reliance agreements and of the 
communication plan.  
Data Analysis 
We will use SPSS Statistics v25 (IBM) for Aim 1 analyses. Analysts will examine 
data for missing values, extreme scores, and variable distributions. We expect missing 
values on the primary outcome to be minimal because study staff will collect these data. 
If the missing values percentage exceeds 5%,101 we will use a multiple imputation 
approach for analyses. For our primary analysis, we will use linear mixed-effects 
regression models102 to test for group differences in fidelity outcomes at the school year 
end, while accounting for classroom nesting within site. Covariates will include state, 
site size, cohort, turnover rate, October fidelity, quality rating, and demographics. The 
statistical significance of the treatment group predictor (alpha =.05) will be used to 
determine significant differences in fidelity outcomes for the low- vs high-intensity 
groups. Additional analyses will include repeated outcomes from all time points to test 
for treatment group differences across time and time-by-treatment effects. We will 
repeat these analyses for secondary implementation outcomes. We will also examine 
child-level outcomes using linear mixed-effects regression models, which account for a 
child’s nesting within classrooms and sites. Parallel to primary analyses, we will first test 
treatment group differences at the spring assessment and then examine treatment and 
time-by-treatment effects using all time points. For all analyses, a significant, positive 
effect of treatment group will support the effectiveness of applying high-intensity 
strategies at sites that do not respond to low-intensity strategies initially. An exploratory 

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analysis is to describe the number of sites that were early responders and maintained 
response until the April assessment (versus regressing to non-responders over time).  
For power analysis, we used Optimal Design software103 to accommodate the 
clustered design of classrooms nested in sites. Our estimated sample size is based on 
the primary fidelity outcome and is analogous to powering a 2-arm randomized 
controlled trial. We have powered our study to detect a practically meaningful 1-point 
difference on our fidelity scale: 1 point would differentiate an educator who implements 
a practice only somewhat (e.g., score of 2) from an educator who implements a practice 
to a significant degree (e.g., score of 3). Based on standard deviations from the K01, a 
1-point difference would yield Cohen’s d effect sizes between .83 (Mascot) and 1.68 
(Role Model). Assuming 64 non-responder sites (assigned 1:1) with an average of 3 
non-responding classrooms per site (192 classrooms), the largest previously observed 
0.20 Interclass Correlation Coefficient (ICC), and 2-sided α =.05, we will have 80% 
power to detect an effect size of d = 0.49 or larger. We do not anticipate site-level 
attrition, but even with ~20% attrition (N = 50 sites), we would have 80% power to detect 
an effect size of d =.56 or greater. Assuming one randomly chosen classroom per site, 
15 children per classroom (N=64*15=960), a 0.10 ICC (largest observed child-level ICC 
in the K01), and 2-sided α =.05, we will have 80% power to detect an effect size of d 
=.29 or larger for child-level outcomes, which corresponds to an effect size of between 
small and medium.104   
Moderator analyses for Aim 2 will be conducted in SPSS using mixed effects 
logistics regression models with a treatment main effect (low- vs high-intensity), the 
moderator main effect (organizational readiness and educator experience), and the 
interaction between the two. The interaction term significance (alpha =.05) will be 
evaluated to test moderation. Models will account for the classroom nesting within sites 
and include controls for state, quality rating, and key demographics. For mediation 
analyses in Aim 2, we will test a multilevel, multiple mediator model in a Structural 
Equation Modeling (SEM) framework105 to account for students clustering in classrooms 
and classrooms within sites using MPLUS software. Specifying a multiple mediator 
model is less biased than testing single mediators one at a time.106 Using parametric 
bootstrapping (Monte Carlo)107,108 significance tests in SEM are also less biased than 
sequential hypothesis testing approaches to mediation tests.109 Using data at three time 
points, we will be able to model that Y (independent variable) precedes M (mediator) in 
time, and M precedes Y (dependent variable) in time; prior levels of M and Y can be 
controlled.For moderator analyses, we will have 80% power detect a Cohen’s f2 of .13 
which falls between a small (f2=.02) and medium (f2=.15) effect size.104 In mediation 
analyses, the indirect effect is the product of 2 regression coefficients and is not 
distributed normally, which poses a challenge to power calculations.110 However, Fritz 
and MacKinnon111 recommend a bias-corrected bootstrap method for the indirect effect 
that, in our sample size of 64 sites, would provide 80% power to detect an indirect effect 
composed of 2 large-sized constituent effects, or a mix of a large-sized and a medium-
sized effects. 
Qualitative analyses for Aim 2 will focus on identifying similarities and differences 
between site response types. Transcripts will be matched with observed field notes and 

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coded using directed content analysis.112 The i-PARIHS framework will provide a 
template of sensitizing concepts to label significant, recurrent ideas,113 particularly ideas 
that suggest emergent candidate mediators and moderators. We will incorporate 
inductive codes as we identify additional salient factors.114 Primary and secondary 
coders (at least 1 each from AR and LA) will code the same transcripts until inter-rater 
reliability is established. Minimum reliability will be set at Kappa of 0.75, which reflects 
excellent agreement between coders.115 Coding will be independent after establishing 
reliability. Coders will hold weekly meetings to discuss iterative expansions to the 
codebook, to reach consensus about unclear codes, and to document tentative patterns 
in the data. A third-party team member will resolve disagreements.116 Participants and 
stakeholders will review site-level summaries of findings. We will conduct analyses of 
qualitative interviews yearly and use findings to revise the interview guide for 
subsequent interviews (e.g., identify probing needs, generate new questions). We will 
use Nvivo software (QSR International) to code and calculate inter-rater reliability. As 
we interpret Aim 2 findings, we will connect quantitative and qualitative data. This will 
include: 1) expansion of quantitative findings to provide detail through qualitative data 
and 2) complementarity to deepen understanding and identify other potential 
moderators and mediators not focused on in quantitative analyses. Thus, qualitative 
data will explain and elaborate on quantitative findings. 
For cost analysis in Aim 3, we will calculate the incremental cost-effectiveness ratio 
in 4 steps:  
Step 1: Aim 1 data will provide estimates of fidelity and child outcome changes (i.e., 
BMI, RRS, target food consumption) for both study conditions. We will aggregate these 
findings to the site. 
Step 2: Calculate the costs associated with implementation at each site. The WISE 
intervention cost is the same at all sites, and the ECE system does not accrue 
downstream costs or benefits. Therefore, we focus on implementation costs only, which 
comprise 4 categories: facilitator salary and benefits, facilitator travel expenses, 
educational resources, and other classrooms/site resources.These will be collected 
using a micro-costing approach, and expenses will be applied to the appropriate site. 
Step 3: Estimate covariates to adjust for site-level differences in fidelity. We will use the 
same covariates as in Aim 1 and aggregate child-level covariates to the site. 
Incremental costs will be calculated using intent-to-treat analysis to measure the effect 
of treatment allocation. We will use generalized linear models (GLMs) to estimate the 
effect of implementation intensity on fidelity, child outcomes, and implementation costs. 
We will impute missing values via the ice procedure in STATA v14.0 (StataCorp LLC). 
We will compute 2 outcome predictions for each site based on the coefficients from the 
GLM regressions and the covariates.117 The first prediction will be as if the site was 
randomized to the high-intensity strategy, and the second prediction will be as if the site 
was randomized to the low-intensity strategy. The difference between these predictions 
represents the incremental effect of the implementation strategy on fidelity, child 
outcomes, or costs.  
Step 4: Calculate the incremental cost-effectiveness of adding the high-intensity 
strategy relative to continuing the low-intensity strategy. The numerator will be the 

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incremental difference in total implementation costs incurred at sites receiving the high-
intensity strategy compared to sites continuing the low-intensity strategy. The 
denominator will include the difference in the changes in fidelity or child outcomes 
between the fall implementation and spring implementation assessments for high-
intensity vs. low-intensity strategies. We will use a nonparametric bootstrap with 
replacement method with 1,000 replications to generate an empirical joint distribution of 
incremental implementation costs and fidelity or child outcome change scores, and 
acceptability curves representing the probability of falling below cost effectiveness 
thresholds identified by stakeholders (COBRE work to be completed by March 2021). 
Analysts will build preliminary models using data from the first cohort (Y2) to promote 
analysis expedience when all three cohorts are completed (Y4). 
 
Ethical Considerations 
This study will be conducted in accordance with all applicable government regulations 
and University of Arkansas for Medical Sciences research policies and 
procedures.  This protocol and any amendments will be submitted and approved by the 
UAMS Institutional Review Board (IRB) to conduct the study.   
A waiver of documentation of informed consent is requested for teachers in the project 
who will only participate only in classroom observational activities. This is because 
WISE will be adopted in all classrooms as a normal educational activity, and open 
classroom observations are standard practice in early care and education. Completion 
of surveys will be assumed as consent; we will include language in the introduction to 
the survey that describes the voluntary and de-identified nature of the survey as well as 
that refusal will not affect employment.  For educators participating in interviews, we will 
collect verbal consent on the audio recording (i.e., we are requesting waiver of written 
consent only). For these aims, the only record linking the subject and the study would 
be the consent document making the principal risk a breach of confidentiality.   This 
research involves no more than minimal risk to the subjects, and waivers will not 
adversely affect the rights and welfare of the subjects.  
 
For child data collection, teachers and staff will hand out packets to each parent at 
enrollment. This packet will contain a study information sheet along with the necessary 
paperwork the parent completes to enroll at the center.  We will submit these materials 
for approval prior to use in the study. The researchers believe that this is a good 
strategy for ensuring that each parent receives and reads the information sheet. The 
parent can return the signed form with the rest of the packet if he/she does not want 
his/her child to participate. We are therefore requesting a waiver of documentation of 
consent (e.g., "passive consent") for child participants. It is possible that some children 
would be wards of the state, although this is not our target population.We have a two-
pronged plan to balance protection of vulnerable children and unnecessary exclusion. 

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First, we will summarize our study and protocol in lay-friendly language and share this 
with the AR and LA state Departments of Human Services (DHS). We will request them 
to sign a letter stating they understand the protocol and approve participation for all 
children that are wards of the state. In the event that we are unable to obtain blanket 
state approval, we will add a check box to the parent information sheet for caregivers to 
indicate they are providing foster care for the child and are unable to provide permission 
(i.e., we would exclude wards of the state from participation). . The study is minimal risk, 
and involves no procedures for which written consent is normally required outside the 
research context.   The parent Information Sheet will be created and submitted for 
approval in the first 6 months of the study while site recruitment efforts are underway. 
 
To collect child assent, children will be given a brief, developmentally-appropriate 
description of study activities (e.g., I would like to see how tall you are.” “This machine 
tells about the color of foods you eat.”). Then, research staff will ask children if they 
would like to participate. Children who state they do not want to participate or exhibit 
non-verbal hesitancy (e.g., crying, non-compliance) will not be pressured further and will 
not be included in the research assessments. Classrooms teachers assist the research 
team with identifying children and inviting them to the research activities. If a child’s parent does 
not want them to participate, the teacher (a known and trusted adult) can explain that they will 
continue with their other normal activities (e.g., “You are having your turn in the block center 
today; your place is right here with me!). In prior studies (#134665 and #205335), this has 
prevented child distress about wanting to participate against their parent’s wishes. This also 
avoids additional interaction with the research team, which may also be against the parent’s 
wishes (versus having the child participate but not recording their data). 
In accordance with the Management Plans for both Drs. Swindle and Whiteside-Mansell, the 
following measures will be taken:  
- 
We will disclose our interests in participant consent and recruitment materials as 
applicable.  
- 
All research partners will receive and sign a copy of the management plans.  
- 
The interest will be disclosed in all publications and presentations.  
- 
All data analysis (both qualitative and quantitative) will be reviewed by non-conflicted co-
investigators or biostatisticians.  
- 
All details in the respective Management Plans will be honored including reports on the 
specified schedule.  
 
Dissemination of Data 
We have developed a dissemination plan that makes data and research resources 
freely available in a timely manner to diverse audiences while safeguarding the privacy 
of participants and protecting confidential information (see Resource Sharing Plan). We 
will register our trial with ClinicalTrials.gov and report our results there within one year of 
the trial completion date.  The process of informed consent will include a statement 

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informing the participants of the posting of our de-identified and aggregated results to 
ClinicalTrials.gov.  The final, anonymized dataset will be made publicly available upon 
reasonable request or at the requirement of journal submission guidelines.  
 
In addition, we will employ a multi-pronged strategy to ensure that findings from this 
research are disseminated to participants, scientists and community stakeholders. 
These efforts will not contain any identifiable information that could be linked to a 
participant.  
 
Study participants. Results will be returned to participants first. We will share back 
findings of all stages of the project to participants and partnering agencies. Each 
participant will be mailed or e-mailed a one-page summary of results. Results will be 
presented in the form of an infographic that uses understandable words, as well as 
culturally relevant pictures/ examples. The study team has incorporated careful 
measures to protect the participants involved in the study. We will disseminate 
aggregate results and ensure that no personal health information (PHI) is ever shared. 
During enrollment, we will ask participants how they want to receive the summary of 
study results (a personal e-mail, or a mailed paper copy), and results will be provided in 
the manner they select. At that time, we will remind participants that the results will be 
shared in aggregate form and that no PHI will be shared. The research team will 
carefully review dissemination materials to reduce the risk of unintentionally identifying 
or stigmatizing participants when sharing results in small communities.  
 
Local Stakeholders. By design, Evidence-Based Quality Improvement (EBQI) members 
will be updated on study progress throughout the study, including at the three planned 
meetings per year. At these meetings, the research team will present updates on 
enrollment and retention. When the study concludes, we will present the results and 
lessons learned. We will use easy to understand tools (e.g., infographics) to present 
results and lessons learned and provide reports stakeholders can distribute to their 
community members.  Aggregate data with no PHI will be shared. 
   
Early Care and Education Stakeholders.  To ensure appropriate dissemination to 
community stakeholders the research team will seek input from stakeholders serving on 
the EBQI panel as well as The Translational Research Institute and the Center for 
Health Literacy (see Facilities and Other Resources). Early educators are a primary 
stakeholder in the findings of this research as well as directors and administrators at 
early care and education agencies.  We will reach stakeholders through presentation at 
local conferences with an early education focus and at other community events 
identified by EBQI members.   
 

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Scientists.  We will disseminate findings to scientists with interests in implementation 
science, obesity prevention, nutrition education, and early childhood development. We 
will attend and present at premier conferences in implementation science and nutrition. 
We will also submit findings for publication to leading journals in the field such as 
Implementation Science, Implementation Science Communications, and Appetite. will 
also share abstracts and publications with academic listservs and professional social 
networking sites on which I am a member. 
 
 
 
 

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References 
1.  
Reid DD, Higginson J. The theoretical possibilities of cancer prevention in man. 
Proc R Soc Med. 1968;61(7):723. 
2.  
Wynder EL, Gori GB. Contribution of the environment to cancer incidence: An 
epidemiologic exercise. J Natl Cancer Inst. Published online 1977. 
doi:10.1093/jnci/58.4.825 
3.  
Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body 
Fatness and Cancer — Viewpoint of the IARC Working Group. N Engl J Med. 
2016;375(8):794-798. doi:10.1056/NEJMsr1606602 
4.  
Whiteman DC, Wilson LF. The fractions of cancer attributable to modifiable 
factors: A global review. Cancer Epidemiol. 2016;44:203-221. 
doi:10.1016/j.canep.2016.06.013 
5.  
Siegel RL, Miller KD, Jemal A. Cancer Statistics in USA , 2018. CA Cancer J Clin. 
Published online 2018. doi:10.3322/caac.21442 
6.  
PDQ Screening and Prevention Editorial Board. Cancer Prevention Overview 
(PDQ®): Patient Version.; 2002. Accessed August 19, 2019. 
http://www.ncbi.nlm.nih.gov/pubmed/26389424 
7.  
Centers for Disease Control and Prevention. Leading cancer cases and deaths, 
Male and female, 2016. United States Cancer Statistics: Data Visualizations. 
Published 2016. https://gis.cdc.gov/Cancer/USCS/DataViz.html 
8.  
Mennella JA. Ontogeny of taste preferences: basic biology and implications for 
health. Am J Clin Nutr. 2014;99(3):704S-711S. doi:10.3945/ajcn.113.067694 
9.  
Simmonds M, Llewellyn A, Owen CG, Woolacott N. Predicting adult obesity from 
childhood obesity: a systematic review and meta-analysis. Obes Rev. 
2016;17(2):95-107. doi:10.1111/obr.12334 
10.  Centers for Disease Control. Progress on childhood obesity. CDC Morb Mortal 
Wkly Rep Vital Signs. Published online August 2013. 
https://www.cdc.gov/vitalsigns/pdf/2013-08-vitalsigns.pdf 
11.  America CCA of. About Child Care. Child Care Aware of America. Published 
2019. https://usa.childcareaware.org/families-programs/about-child-care/ 
12.  Child Care Aware of America. Checking in: A Snapshot of the Child Care 
Landscape.; 2017. Accessed May 8, 2018. http://usa.childcareaware.org/wp-
content/uploads/2017/07/FINAL_SFS_REPORT.pdf 
13.  Larson N, Ward DS, Neelon SB, Story M. What role can child-care settings play in 
obesity prevention? A review of the evidence and call for research efforts. J Am 
Diet Assoc. 2011;111(9):1343-1362. doi:10.1016/j.jada.2011.06.007 
14.  World Cancer Research Fund International. Diet, Nutrition, Physical Activity and 
Cancer: A Global Perspective.; 2018. https://www.wcrf.org/dietandcancer/about 
15.  Whiteside-Mansell L, Swindle TM. Together We Inspire Smart Eating: A 
Preschool Curriculum for Obesity Prevention in Low-Income Families. J Nutr Educ 
Behav. 2017;49(9):789-792.e1. doi:10.1016/j.jneb.2017.05.345 
16.  Whiteside-Mansell L, Swindle T. Together We Inspire Smart Eating: Foundation, 
Feasibility and Acceptance of a WISE curriculum for obesity prevention. Glob 
Pediatr Heal. 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 21 
 
17.  SNAP-Ed Toolkit. Together, We Inspire Smart Eating (WISE). Obesity Prevention 
Interventions and Evaluation Framework. Published 2019. Accessed March 24, 
2020. https://snapedtoolkit.org/interventions/programs/together-we-inspire-smart-
eating-wise/ 
18.  Mustonen S, Rantanen R, Tuorila H. Effect of sensory education on school 
children’s food perception: A 2-year follow-up study. Food Qual Prefer. 
2009;20(3):230-240. doi:10.1016/j.foodqual.2008.10.003 
19.  Anzman-Frasca S, Savage JS, Marini ME, Fisher JO, Birch LL. Repeated 
exposure and associative conditioning promote preschool children’s liking of 
vegetables. Appetite. 2012;58(2):543-553. doi:10.1016/j.appet.2011.11.012 
20.  Reverdy C, Chesnel F, Schlich P, Köster EP, Lange C. Effect of sensory 
education on willingness to taste novel food in children. Appetite. 2008;51(1):156-
165. doi:10.1016/j.appet.2008.01.010 
21.  Knai C, Pomerleau J, Lock K, McKee M. Getting children to eat more fruit and 
vegetables: a systematic review. Prev Med (Baltim). 2006;42(2):85-95. 
doi:10.1016/j.ypmed.2005.11.012 
22.  Wardle J, Herrera M-L, Cooke L, Gibson EL. Modifying children’s food 
preferences: the effects of exposure and reward on acceptance of an unfamiliar 
vegetable. Eur J Clin Nutr. 2003;57(2):341-348. doi:10.1038/sj.ejcn.1601541 
23.  Wardle J, Chida Y, Gibson EL, Whitaker KL, Steptoe A. Stress and Adiposity: A 
Meta-Analysis of Longitudinal Studies. Obesity. 2011;19(4):771-778. 
doi:10.1038/oby.2010.241 
24.  Schindler JM, Corbett D, Forestell CA. Assessing the effect of food exposure on 
children’s identification and acceptance of fruit and vegetables. Eat Behav. 
2013;14(1):53-56. doi:10.1016/j.eatbeh.2012.10.013 
25.  Hendy HM, Raudenbush B. Effectiveness of teacher modeling to encourage food 
acceptance in preschool children. Appetite. 2000;34(1):61-76. 
doi:10.1006/appe.1999.0286 
26.  Hendy HM. Comparison of five teacher actions to encourage children’s new food 
acceptance. Ann Behav Med. 1999;21(1):20-26. doi:10.1007/BF02895029 
27.  Gibson EL, Kreichauf S, Wildgruber A, et al. A narrative review of psychological 
and educational strategies applied to young children’s eating behaviours aimed at 
reducing obesity risk. Obes Rev. 2012;13 Suppl 1:85-95. doi:10.1111/j.1467-
789X.2011.00939.x 
28.  Galloway AT, Fiorito LM, Francis LA, Birch LL. “Finish your soup”: 
counterproductive effects of pressuring children to eat on intake and affect. 
Appetite. 2006;46(3):318-323. doi:10.1016/j.appet.2006.01.019 
29.  Birch LL, McPheee L, Shoba B., Steinberg L, Krehbiel R. “Clean up your plate”: 
Effects of child feeding practices on the conditioning of meal size. Learn Motiv. 
1987;18(3):301-317. doi:10.1016/0023-9690(87)90017-8 
30.  Borzekowski D, Robinson T. The 30-second effect: an experiment revealing the 
impact of television commercials on food preferences of preschoolers. J Am Diet. 
Published online 2001. Accessed October 21, 2016. 
http://www.sciencedirect.com/science/article/pii/S0002822301000128 
31.  Boyland E, Harrold J, Kirkham T, Halford J. Persuasive techniques used in 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 22 
 
television advertisements to market foods to UK children. Appetite. 
2012;58(2):658-664. 
32.  Kraak V, Story M. Influence of food companies’ brand mascots and entertainment 
companies’ cartoon media characters on children’s diet and health: a systematic 
review and. Obes Rev. Published online 2015. Accessed October 21, 2016. 
http://onlinelibrary.wiley.com/doi/10.1111/obr.12237/full 
33.  Keller K, Kuilema L, Lee N, Yoon J, Mascaro B. The impact of food branding on 
children’s eating behavior and obesity. Physiol. Published online 2012. Accessed 
October 21, 2016. 
http://www.sciencedirect.com/science/article/pii/S0031938412001163 
34.  Roberto C, Baik J, Harris J, Brownell K. Influence of licensed characters on 
children’s taste and snack preferences. Pediatrics. Published online 2010. 
Accessed October 21, 2016. 
http://pediatrics.aappublications.org/content/126/1/88.short 
35.  Weber K, Story M, Harnack L. Internet food marketing strategies aimed at 
children and adolescents: a content analysis of food and beverage brand web 
sites. J Am Diet Assoc. 2006;106(9):1463-1466. 
36.  Benjamin-Neelon SE. Position of the Academy of Nutrition and Dietetics: 
Benchmarks for Nutrition in Child Care. J Acad Nutr Diet. Published online 2018. 
doi:10.1016/j.jand.2018.05.001 
37.  Whiteside-Mansell L, Swindle TM. Evaluation of Together We Inspire Smart 
Eating: pre-school fruit and vegetable consumption. Health Educ Res. 
2019;34(1):62-71. doi:10.1093/her/cyy048 
38.  Whiteside-Mansell L, Swindle T, Davenport K. Evaluation of “Together, We 
Inspire Smart Eating” (WISE) nutrition intervention for young children: 
Assessment of fruit and vegetable consumption with parent reports and 
measurements of skin carotenoids as biomarkers. J Hunger Environ Nutr. 
39.  Rutledge, J. M., Swindle, T., McBride, N., Selig, J. P., Percle, P. M., & Whiteside-
Mansell L. Home-based health outcomes of a childcare-based nutrition 
intervention: Pester power examined. In: International Society of Behavioral 
Nutrition and Physical Activity. ; 2019. 
40.  Swindle T, Johnson SL, Whiteside-Mansell L, Curran GM. A mixed methods 
protocol for developing and testing implementation strategies for evidence-based 
obesity prevention in childcare: a cluster randomized hybrid type III trial. 
Implement Sci. 2017;12(1):90. doi:10.1186/s13012-017-0624-6 
41.  Benjamin SE, Ammerman A, Sommers J, Dodds J, Neelon B, Ward DS. Nutrition 
and Physical Activity Self-assessment for Child Care (NAP SACC): Results from a 
Pilot Intervention. J Nutr Educ Behav. 2007;39(3):142-149. 
doi:10.1016/j.jneb.2006.08.027 
42.  Wolfenden L, Jones J, Williams C. Strategies to improve the implementation of 
healthy eating, physical activity and obesity prevention policies, practices or 
programmes within childcare services. The Cochrane. Published online 2016. 
Accessed August 7, 2017. 
http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD011779.pub2/full 
43.  Byrd-Williams CE, Dooley EE, Thi CA, Browning C, Hoelscher DM. Physical 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
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Page 23 
 
activity, screen time, and outdoor learning environment practices and policy 
implementation: A cross sectional study of Texas child care centers. BMC Public 
Health. 2019;19(1):1-11. doi:10.1186/s12889-019-6588-5 
44.  Damschroder LJ. Clarity out of chaos: Use of theory in implementation research. 
Psychiatry Res. Published online June 23, 2019. 
doi:10.1016/J.PSYCHRES.2019.06.036 
45.  Harvey G, Kitson A. Facilitation as the active ingredient. In: Implementing 
Evidence-Based Practice In Healthcare: A Facilitation Guide. Routledge; 
2015:169-184. doi:10.4324/9780203557334-11 
46.  Ullrich PM, Sahay A, Stetler CB. Use of Implementation Theory: A Focus on 
PARIHS.; 2000. Accessed June 27, 2019. 
https://sigmapubs.onlinelibrary.wiley.com/doi/pdf/10.1111/wvn.12016 
47.  Helfrich CD, Damschroder LJ, Hagedorn HJ, et al. A critical synthesis of literature 
on the promoting action on research implementation in health services (PARIHS) 
framework. Implement Sci. 2010;5(1):82. doi:10.1186/1748-5908-5-82 
48.  Harvey G, Loftus-Hills A, Rycroft-Malone J, et al. Getting evidence into practice: 
The role and function of facilitation. J Adv Nurs. Published online 2002. 
doi:10.1046/j.1365-2648.2002.02126.x 
49.  Baskerville NB, Liddy C, Hogg W. Systematic review and meta-analysis of 
practice facilitation within primary care settings. Ann Fam Med. Published online 
2012. doi:10.1370/afm.1312 
50.  Culler SD, Parchman ML, Lozano-Romero R, et al. Cost estimates for operating a 
primary care practice facilitation program. Ann Fam Med. Published online 2013. 
doi:10.1370/afm.1496 
51.  Almirall D, Nahum-Shani I, Wang L, Kasari C. Experimental Designs for Research 
on Adaptive Interventions: Singly and Sequentially Randomized Trials. In: Collins 
LM, Kugler KC, eds. Optimization of Behavioral, Biobehavioral, and Biomedical 
Interventions. Springer; 2018. doi:10.1007/978-3-319-91776-4_4 
52.  Almirall D, Chronis-Tuscano A. Adaptive Interventions in Child and Adolescent 
Mental Health. J Clin Child Adolesc Psychol. 2016;45(4):383-395. 
doi:10.1080/15374416.2016.1152555 
53.  Swindle T, Johnson SL, Davenport K, et al. A Mixed-Methods Exploration of 
Barriers and Facilitators to Evidence-Based Practices for Obesity Prevention in 
Head Start. J Nutr Educ Behav. Published online 2019. 
doi:10.1016/j.jneb.2019.06.019 
54.  Rida Z, Burger C, Dev D, Smith J, Hasnin S. Assessment of Nutrition Knowledge 
of Childcare Providers Regarding the Implementation of the 2017 CACFP Meal 
Pattern Update. Am J Heal Educ. 2018;49(6):384-394. 
doi:10.1080/19325037.2018.1527734 
55.  Lee DL, Gurzo K, Yoshida S, Homel Vitale E, Hecht K, Ritchie LD. Compliance 
with the New 2017 Child and Adult Care Food Program Standards for Infants and 
Children before Implementation. Child Obes. 2018;14(6):393-402. 
doi:10.1089/chi.2018.0092 
56.  Food and Research Action Center. Child & Adult Care Food Program: 
Participation Trends 2018.; 2019. Accessed August 12, 2020. https://frac.org/wp-

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 24 
 
content/uploads/CACFP-participation-trends-2018.pdf 
57.  United States Department of Agriculutre. Child Day Care Centers | USDA-FNS. 
Accessed August 11, 2020. https://www.fns.usda.gov/cacfp/child-day-care-
centers 
58.  Proctor E, Silmere H, Raghavan R, et al. Outcomes for Implementation Research: 
Conceptual Distinctions, Measurement Challenges, and Research Agenda. Adm 
Policy Ment Heal Ment Heal Serv Res. 2011;38(2):65-76. doi:10.1007/s10488-
010-0319-7 
59.  Weiner BJ, Lewis CC, Stanick C, et al. Psychometric assessment of three newly 
developed implementation outcome measures. Implement Sci. 2017;12(1):108. 
doi:10.1186/s13012-017-0635-3 
60.  Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference 
Manual. Human Kinetics Books; 1988. 
61.  Darvin M, Sterry W, Lademann J, Vergou T. The role of carotenoids in human 
skin. Molecules. 2011;16(2):1710-1738. Accessed October 24, 2016. 
http://www.mdpi.com/1420-3049/16/12/10491 
62.  Ermakov I V., Gellermann W. Dermal carotenoid measurements via pressure 
mediated reflection spectroscopy. J Biophotonics. 2012;5(7):559-570. 
doi:10.1002/jbio.201100122 
63.  Scarmo S, Henebery K, Peracchio H, et al. Skin carotenoid status measured by 
resonance Raman spectroscopy as a biomarker of fruit and vegetable intake in 
preschool children. Eur J Clin Nutr. 2012;66(5):555-560. doi:10.1038/ejcn.2012.31 
64.  Blume-Peytavi U, Rolland A, Darvin ME, et al. Cutaneous lycopene and β-
carotene levels measured by resonance Raman spectroscopy: High reliability and 
sensitivity to oral lactolycopene deprivation and supplementation. Eur J Pharm 
Biopharm. 2009;73(1):187-194. doi:10.1016/j.ejpb.2009.04.017 
65.  Meinke M, Darvin M, Vollert H, Lademann. Bioavailability of natural carotenoids in 
human skin compared to blood. Eur J Pharm Biopharm. 2010;76(2):269-274. 
Accessed October 24, 2016. 
http://www.sciencedirect.com/science/article/pii/S0939641110001530 
66.  Centers for Disease Control and Prevention. CDC Growth Charts: United States; 
Length-for-Age Percentiles: Boys, Birth to 36 Months.; 2000. 
67.  Whiteside-Mansell L, Bradley R, Conners N, Bokony P. The Family Map: 
Structured Family Interview to Identify Risks and Strengths in Head Start 
Families. NHSA Dialog. 2007;10(3-4):189-209. Accessed November 13, 2015. 
http://www.tandfonline.com/doi/abs/10.1080/15240750701742239#.VkYn_XarTm
E 
68.  Bellows LL, Johnson SL, Davies PL, Anderson J, Gavin WJ, Boles RE. The 
Colorado LEAP study: Rationale and design of a study to assess the short term 
longitudinal effectiveness of a preschool nutrition and physical activity program. 
BMC Public Health. 2013;13(1). doi:10.1186/1471-2458-13-1146 
69.  Johnson SL, Ryan SM, Kroehl M, Moding KJ, Boles RE, Bellows LL. A 
longitudinal intervention to improve young children’s liking and consumption of 
new foods: Findings from the Colorado LEAP study. Int J Behav Nutr Phys Act. 
2019;16(1). doi:10.1186/s12966-019-0808-3 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 25 
 
70.  Ritchie, M. J., Dollar, K. M., Miller, C. J., Oliver, K. A., Smith, J. L., Lindsay, J. A., 
& Kirchner JE. Using Implementation Facilitation to Improve Care in the Veterans 
Health Administration (Version 2). Veterans Health Administration, Quality 
Enhancement Research Initiative (QUERI) for Team-Based Behavioral Health; 
2017. https://www.queri.research.va.gov/tools/implementation/Facilitation-
Manual.pdf 
71.  Smith JL, Ritchie MJ, Kim B, et al. Scoping Review to Identify the Spectrum of 
Activities Applied in Implementation Facilitation Strategies. In: 2nd International 
Conference on Practice Facilitation. ; 2018. 
72.  Green CA, Duan N, Gibbons RD, Hoagwood KE, Palinkas LA, Wisdom JP. 
Approaches to Mixed Methods Dissemination and Implementation Research: 
Methods, Strengths, Caveats, and Opportunities. Adm Policy Ment Heal Ment 
Heal Serv Res. Published online 2014. doi:10.1007/s10488-014-0552-6 
73.  Creswell JW, Klassen AC, Clark VLP, Smith KC. Best Practices for Mixed 
Methods Research in the Health Sciences.; 2011. 
74.  Papakostas GI, Fava M. Predictors, Moderators, and Mediators (Correlates) of 
Treatment Outcome in Major Depressive Disorder.; 2008. Accessed October 26, 
2020. www.dialogues-cns.org 
75.  Almirall D, Mccaffrey DF, Ramchand R, Corporation R, Murphy SA, Author PS. 
Subgroups Analysis when Treatment and Moderators are Time-varying NIH 
Public Access Author Manuscript. Prev Sci. 2013;14(2):169-178. 
doi:10.1007/s11121-011-0208-7 
76.  Rucker DD, Preacher KJ, Tormala ZL, Petty RE. Mediation Analysis in Social 
Psychology: Current Practices and New Recommendations. Soc Personal 
Psychol Compass. 2011;5(6):359-371. doi:10.1111/j.1751-9004.2011.00355.x 
77.  Memon MA, Cheah J, Ramayah T, Ting H, Chuah F. Mediation Analysis: Issues 
and Recommendations. J Appl Struct Equ Model. 2018;2(1):1-9. Accessed July 
11, 2019. https://www.researchgate.net/publication/322293701 
78.  Fedak KM, Bernal A, Capshaw ZA, Gross S. Applying the Bradford Hill criteria in 
the 21st century: How data integration has changed causal inference in molecular 
epidemiology. Emerg Themes Epidemiol. 2015;12(1):14. doi:10.1186/s12982-
015-0037-4 
79.  Benzer JK, Young G, Stolzmann K, et al. The relationship between organizational 
climate and quality of chronic disease management. Health Serv Res. 
2011;46(3):691-711. doi:10.1111/j.1475-6773.2010.01227.x 
80.  Mohr DC, Benzer JK, Young GJ. Provider Workload and Quality of Care in 
Primary Care Settings: Moderating Role of Relational Climate. Med Care. 
2013;51:108-114. doi:10.2307/41714659 
81.  Smith SN, Almirall D, Prenovost K, et al. Organizational culture and climate as 
moderators of enhanced outreach for persons with serious mental illness: results 
from a cluster-randomized trial of adaptive implementation strategies. Implement 
Sci. 2018;13(1):93. doi:10.1186/s13012-018-0787-9 
82.  Freedman MR, Alvarez KP. Early childhood feeding: assessing knowledge, 
attitude, and practices of multi-ethnic child-care providers. J Am Diet Assoc. 
2010;110(3):447-451. doi:10.1016/j.jada.2009.11.018 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 26 
 
83.  Swindle T, Patterson Z, Boden McGill C. A qualitative application of the Belsky 
Model to explore early care and education teachers’ nutritional developmental 
history, beliefs, and teaching interactions. J Nutr Educ Behav. 
84.  Dev DA, Speirs KE, McBride BA, Donovan SM, Chapman-Novakofski K. Head 
Start and child care providers’ motivators, barriers and facilitators to practicing 
family-style meal service. Early Child Res Q. 2014;29(4):649-659. 
doi:10.1016/j.ecresq.2014.07.004 
85.  Swindle TM, Ward WL, Bokony P, Whiteside-Mansell L. A Cross-Sectional Study 
of Early Childhood Educators’ Childhood and Current Food Insecurity and Dietary 
Intake. J Hunger Environ Nutr. 2018;13(1):40-54. 
doi:10.1080/19320248.2016.1227752 
86.  Helfrich C, Li Y, Sharp N, Sales A. Organizational readiness to change 
assessment (ORCA): development of an instrument based on the Promoting 
Action on Research in Health Services (PARIHS) Framework. Implement Sci. 
2009;4:38. Accessed October 24, 2016. 
http://implementationscience.biomedcentral.com/articles/10.1186/1748-5908-4-38 
87.  Lewis CC, Klasnja P, Powell BJ, et al. From Classification to Causality: Advancing 
Understanding of Mechanisms of Change in Implementation Science. Front Public 
Heal. 2018;6:136. doi:10.3389/fpubh.2018.00136 
88.  Beidas RS, Becker-Haimes EM, Adams DR, et al. Feasibility and acceptability of 
incentive-based implementation strategies for mental health therapists 
implementing cognitive-behavioral therapy: A pilot study to inform a randomized 
controlled trial. Implement Sci. 2017;12(1). doi:10.1186/s13012-017-0684-7 
89.  Berta W, Cranley L, Dearing JW, Dogherty EJ, Squires JE, Estabrooks CA. Why 
(we think) facilitation works: insights from organizational learning theory. 
Implement Sci. 2015;10(1):141. doi:10.1186/s13012-015-0323-0 
90.  Powell BJ, Mandell DS, Hadley TR, et al. Are general and strategic measures of 
organizational context and leadership associated with knowledge and attitudes 
toward evidence-based practices in public behavioral health settings? A cross-
sectional observational study. Implement Sci. Published online 2017. 
doi:10.1186/s13012-017-0593-9 
91.  Turner K, Trogdon JG, Weinberger M, et al. Testing the organizational theory of 
innovation implementation effectiveness in a community pharmacy medication 
management program: A hurdle regression analysis. Implement Sci. Published 
online 2018. doi:10.1186/s13012-018-0799-5 
92.  Beidas RS, Williams NJ, Becker-Haimes EM, et al. A repeated cross-sectional 
study of clinicians’ use of psychotherapy techniques during 5 years of a system-
wide effort to implement evidence-based practices in Philadelphia. Implement Sci. 
Published online 2019. doi:10.1186/s13012-019-0912-4 
93.  Aarons GA, Ehrhart MG, Farahnak LR. The implementation leadership scale 
(ILS): development of a brief measure of unit level implementation leadership. 
Implement Sci. 2014;9(1):45. doi:10.1186/1748-5908-9-45 
94.  Shuman CJ, Liu X, Aebersold ML, Tschannen D, Banaszak-Holl J, Titler MG. 
Associations among unit leadership and unit climates for implementation in acute 
care: a cross-sectional study. Implement Sci. Published online 2018. 

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 27 
 
doi:10.1186/s13012-018-0753-6 
95.  Torrey WC, Bond GR, McHugo GJ, Swain K. Evidence-based practice 
implementation in community mental health settings: The relative importance of 
key domains of implementation Activity. Adm Policy Ment Heal Ment Heal Serv 
Res. Published online 2012. doi:10.1007/s10488-011-0357-9 
96.  Ehrhart MG, Aarons GA, Farahnak LR. Assessing the organizational context for 
EBP implementation: the development and validity testing of the Implementation 
Climate Scale (ICS). Implement Sci. 2014;9(1):157. doi:10.1186/s13012-014-
0157-1 
97.  Lyon AR, Cook CR, Brown EC, et al. Assessing organizational implementation 
context in the education sector: Confirmatory factor analysis of measures of 
implementation leadership, climate, and citizenship. Implement Sci. Published 
online 2018. doi:10.1186/s13012-017-0705-6 
98.  Krueger R, Casey M. Focus Groups: A Practical Guide for Applied Research - 
Richard A. Krueger, Mary Anne Casey - Google Books. Sage Publications; 2014. 
Accessed May 28, 2018. 
https://books.google.com/books?hl=en&lr=&id=tXpZDwAAQBAJ&oi=fnd&pg=PT7
&dq=Krueger+RA,+Casey+MA.+Focus+Groups:+A+Practical+Guide+for+Applied
+Research.+Thousand+Oaks,+CA:+Sage+Publications%3B+2014.&ots=Psmb7T
gz8x&sig=pDsBUtRTq6MIJnSsXv05LbXK3b4#v=onepage&q& 
99.  Ritchie MJ, Liu CF, Townsend JC, Pitcock J, Kirchner JE. Time and cost of 
“Extreme” Implementation Facilitiation to Adddress Challenging Contexts. In: 
Soceity for Implementation Research Collaborative. ; 2017. 
100.  Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research 
electronic data capture (REDCap)-A metadata-driven methodology and workflow 
process for providing translational research informatics support. J Biomed Inform. 
2009;42(2):377-381. doi:10.1016/j.jbi.2008.08.010 
101.  Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple 
imputation be used for handling missing data in randomised clinical trials - A 
practical guide with flowcharts. BMC Med Res Methodol. 2017;17(1):162. 
doi:10.1186/s12874-017-0442-1 
102.  Snijders TAB, Bosker RJ. MULTILEVEL ANALYSIS An introduction to basic and 
advanced multilevel modeling. Published online 1999. Accessed May 5, 2017. 
https://www.researchgate.net/profile/Tom_Snijders2/publication/44827177_Multile
vel_Analysis_An_Introduction_to_Basic_and_Advanced_Multilevel_Modeling/link
s/0c96051ffabd4ca210000000/Multilevel-Analysis-An-Introduction-to-Basic-and-
Advanced-Multilevel-Modeling.pdf 
103.  Spybrook J, Raudenbush SW, Liu X-F, Congdon R, Martínez A. Optimal Design 
for Longitudinal and Multilevel Research: Documentation for the &quot; Optimal 
Design &quot; Software.; 2008. Accessed March 12, 2018. 
http://people.cehd.tamu.edu/~okwok/epsy652/OD/od-manual-20080312-v176.pdf 
104.  Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. L. 
Erlbaum Associates; 1988. Accessed May 24, 2018. 
http://www.worldcat.org/title/statistical-power-analysis-for-the-behavioral-
sciences/oclc/17877467 

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Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 28 
 
105.  Preacher KJ, Zyphur MJ, Zhang Z. A general multilevel SEM framework for 
assessing multilevel mediation. Psychol Methods. Published online 2010. 
doi:10.1037/a0020141 
106.  Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and 
comparing indirect effects in multiple mediator models. Behav Res Methods. 
2008;40(3):879-891. doi:10.3758/BRM.40.3.879 
107.  Preacher KJ, Selig JP. Advantages of Monte Carlo Confidence Intervals for 
Indirect Effects. Commun Methods Meas. 2012;6(2):77-98. 
doi:10.1080/19312458.2012.679848 
108.  Preacher KJ, Zyphur MJ, Zhang Z. A general multilevel SEM framework for 
assessing multilevel mediation. Psychol Methods. 2010;15(3):209-233. 
doi:10.1037/a0020141 
109.  MacKinnon DP, Fairchild AJ, Fritz MS. Mediation Analysis. Annu Rev Psychol. 
2007;58(1):593-614. doi:10.1146/annurev.psych.58.110405.085542 
110.  Selig JP, Preacher KJ. Mediation Models for Longitudinal Data in Developmental 
Research. Res Hum Dev. Published online 2009. 
doi:10.1080/15427600902911247 
111.  Fritz MS, MacKinnon DP. Required sample size to detect the mediated effect. 
Psychol Sci. Published online 2007. doi:10.1111/j.1467-9280.2007.01882.x 
112.  Hsieh H-F, Shannon SE. Three Approaches to Qualitative Content Analysis. Qual 
Health Res. 2005;15(9):1277-1288. doi:10.1177/1049732305276687 
113.  Crabtree B, Miller W. A template approach to text analysis: Developing and using 
codebooks. In: Crabtree B, Miller L, eds. Doing Qualitative Research. 3rd ed. 
SAGE Publications; 1992. Accessed October 23, 2020. 
https://psycnet.apa.org/record/1992-97742-005 
114.  Fereday J, Muir-Cochrane E. Demonstrating Rigor Using Thematic Analysis: A 
Hybrid Approach of Inductive and Deductive Coding and Theme Development. Int 
J Qual Methods. 2006;5(1):80-92. doi:10.1177/160940690600500107 
115.  Fleiss J. The measurement of interrater agreement. Stat methods rates 
proportions. Published online 1981. 
116.  Syed M, Nelson SC. Guidelines for Establishing Reliability When Coding 
Narrative Data. Emerg Adulthood. Published online 2015:1-13. 
doi:10.1177/2167696815587648 
117.  Hoomans T, Severens JL. Economic evaluation of implementation strategies in 
health care. Implement Sci. 2014;9(1):168. doi:10.1186/s13012-014-0168-y 
 
 

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APPENDIX A: DATA AND SAFETY MONITORING PLAN 
The Data and Safety Monitoring Plan (DSMP) outlined below will adhere to a protocol approved 
by the University of Arkansas for Medical Science (UAMS) IRB. The Louisiana Tech IRB and 
University of Colorado Anschutz Medical Campus IRB have agreed that the UAMS IRB will 
provide oversight for the study. Oversight of the DSM Plan will be provided by a locally 
appointed DSM Board under the UAMS IRB. The DSM Board will review data on such aspects 
as site enrollment, site visits, study procedures, forms completion, data quality, site attrition, and 
other measures of adherence to protocol. The DSM Board will provide recommendations to the 
PI regarding potential problems. They will provide periodic reports to the PI indicating whether 
they see any reasons for change and document all their actions. The Chair of the DSM Board 
will notify the UAMS IRB at 501-686-5667 of any concerns consistent with IRB policy 10.2. The 
IRB and NIH will also be notified at the time of renewal of the frequency of monitoring, 
cumulative data, summary of reviews to ensure subject privacy and research data 
confidentiality, and any changes to anticipated benefit-to-risk ratio of study participation to 
minimize research associated risk.  
 
Monitoring Frequency 
The table below provides information on the timeline of our review for each type of data. 
 
Data type 
Frequency 
of review 
Sources 
Reviewer 
Subject recruitment 
(including compliance with 
protocol enrollment 
criteria) 
Quarterly 
Electronic files of 
observations, surveys, 
audio recordings, and 
interviews 
PI, Biostatistician 
Status of data collection 
plan   
Quarterly 
Electronic data files 
PI, DSM Board, 
Biostatistician 
Data regarding delivery of 
implementation support 
Quarterly 
Electronic files of 
materials delivered 
PI, DSM Board 
Unanticipated problems, 
adverse events, and 
serious adverse events 
Per 
occurrence 
 
PI, DSM Board 
UAMS IRB Program 
Manager, NIH 
Reporting Process 
The PI will report adverse events (AE) to the IRB and serious adverse events (SAEs) to the IRB 
and NIH in a timely fashion. In the event that the IRB takes an action that affects the day-to-day 
operations of the study, the PI will report those actions to the NIH Program officer in writing.  
1. In accordance with the UAMS IRB AE reporting policy, an AE is reportable is it meets the 
following criteria: 
 
a) Local adverse events that the investigator determines are: a. unexpected; b. related to 
the research; and c. involve new or increased risks to subjects or others. An event must 
meet all three criteria to require reporting under this policy. 
b) Non-local adverse events that have been determined to be unanticipated problems 
involving risks to subjects or others. 
c) Unanticipated adverse device effects. 
d) Any change or deviation made to the research without prior IRB approval in order to 

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eliminate apparent immediate harm. 
e) An accidental or unintentional change to the IRB-approved protocol that placed one or 
more subjects at increased risk or affects the rights and welfare of subjects or others. 
f) Any new information that indicates an unexpected change to the risks or potential 
benefits of the research. This includes, but is not limited to: a. revised investigator 
brochures, package inserts, device manuals; b. publications in the literature; c. data and 
safety monitoring reports; d. interim results or other findings;  
g) A breach in confidentiality that may involve risk to subjects or others. Examples include 
the loss of a laptop computer on which subject identifiers are stored or the loss of study 
records on a thumb drive. 
h) Any complaint of a participant that indicates an unanticipated risk or any complaint that 
cannot be resolved by the research team. 
i) Incarceration of a subject if study was not previously reviewed with the anticipation of 
enrolling prisoners. 
j) Change in FDA labeling or withdrawal from marketing of a drug, device, or biologic used 
in a research protocol. 
k) Restrictions, suspension or termination of study by the sponsor, investigator, funding 
agency, regulatory body, or institutional administration. 
l) The premature completion of a study. 
m) Notifications of pending audits, inquiries, or investigations by federal agencies. 
n) Written reports from study monitor that include information that requires reporting 
under this policy. 
o) Any other problem that was unexpected, related to the research and places the 
subject or others at a greater risk than previously known. 
 
An example of an AE relevant to this study would be a data breach, and we have taken 
numerous steps to prevent this (See Protection of Human Subjects). Were someone to gain 
access to the data, they would not be able to identify the participants because these files use 
subject numbers only. Should this unlikely event happen, UAMS IRB reporting procedures 
would be followed. Steps would then be taken to identify how the breach occurred and what 
needs to be done to correct it. Additionally, we will follow any recommendations from the Safety 
Officer or IRB. An example of an SAE is the possibility of emotional distress at answering 
survey questions. Participants are not required to answer any questions they choose not to 
answer or that they are uncomfortable answering, and this will be reiterated to participants. The 
information provided by the subjects is confidential, and only study staff have access to 
answers. An example of an unanticipated problem would be if the subject had an unexpected 
negative response to participation in the study. The participant would be counseled as 
appropriate and excluded from the study if needed. These events and problems will be reported 
in accordance with the IRB and NIH policy. 
 
Additional unknown risks may occur and, if so, will be identified through diligent monitoring by 
the PI throughout the study. During the informed consent process, participants will be advised of 
the potential minimum risks of participation and reminded throughout the study that the 
researchers should be promptly informed about any concerns regarding potential side effects or 
AEs. Participants will also be instructed to notify the PI and/or designee of any suspected AEs 
immediately if possible. The PI will maintain an electronic record of all reported AEs and notify 
the Chair of the DSM Board of all reportable events as they occur.. Additionally, the study 
manager will generate and provide de-identified cumulative administrative human participant 
semi-annual safety reports for the DSM Board to review.   

Title:  Testing an Adaptive Implementation Strategy to Optimize Delivery of Obesity Prevention 
Practices in Early Care and Education Settings 
PI:  Taren Swindle 
 
Version #:  V2.0 
Date:  8.23.21  
Page 31 
 
 
Within 72 hours after a reportable AE, SAE or unanticipated problem has been reported by the 
participant, it will be graded by the PI, forwarded to the study’s DSM Board for review, and then 
submitted by the PI to the UAMS IRB. After IRB review and acknowledgment, the PI will forward 
a copy of the reportable AE, SAE or unanticipated problem and IRB acknowledgment letter to 
the NIH Program Officer through the UAMS Office of Research within 72 hours. In addition, all 
cumulative reportable AE, SAE and unanticipated problems included in the DSM reports will be 
submitted to NIH in the Annual Progress Reports.  
Trial Monitoring and Advising 
 We will form a group of independent investigators and biostatisticians under the guidance of the 
UAMS IRB to meet quarterly to review study progress, potential risks, and adherence to 
protocol and timeline.  We expect the group to consist of 3 to 4 individuals with unique 
expertise. We expect to include investigators with expertise in implementation science, research 
involving children, and research design/statistics.