A Skills-based RCT for Physical Activity Using Peer Mentors
Phase: NA
Conditions: Health Behavior, Obesity, Motor Activity
Sponsor: Ohio State University

 
 
 
 
 
 
 
 
 
A Skills-based RCT for Physical Activity Using Peer Mentors 
NCT02329262 
2/15/2015 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Research Protocol : A Skill-Based RCT for Physical Activity Using Peer Mentors 
I. 
Objectives/Aims of the Study: 
The long-term goal of this study is to positively impact the physical activity patterns to improve health outcomes 
including the high rates of obesity in Appalachian teens. For this RCT, we will train peer mentors to deliver the 
culturally appropriate intervention and provide social support that is critical for facilitating and sustaining health 
behavior change. Our objective is to compare the efficacy of an innovative healthy lifestyle skills mentoring 
program (Mentored Planning to be Active [MBA]) to a teacher led program (PBA) for increasing physical activity 
in Appalachian high school teens. Our specific aims are: 
 
Aim 1: Compare the efficacy of peer-led delivery (MBA) to teacher-led delivery (PBA) on health outcomes.  
a. Examine the relationship between health behaviors (sugar sweetened beverage consumption, 
sleep patterns, and health outcomes (body mass index, body fat  at baseline) 
Aim 2: Compare the efficacy of peer-led delivery (MBA) to teacher-led delivery (PBA) on physical activity 
behavior outcomes of Appalachian teens. 
Aim 3: Examine the behavioral impact of the mentoring approach on teen mentors and teachers.  
Aim 4: Examine the usefulness, ease of use, general satisfaction, and suggested refinements of the approach 
by the teen mentors and teachers.  
AIM 5: Determine if self-regulation skills and exercise self-efficacy mediate the relationship between health 
outcomes and moderate/vigorous physical activity. 
 
 
II. 
Background and Rationale: 
Background 
Rural Appalachian populations have poorer health and fewer positive health-related behaviors1-4 compared to 
other United States populations .5-6 Appalachians are the most sedentary population in the U.S.,7-8 and teens are 
particularly sedentary. Only 13.6% of adolescents reported 60 minutes of daily moderate activity, while 38% 
reported no moderate physical activity in the past week and 78.2% reported no vigorous physical activity in the 
past week.1,2,9 Obesity prevention through improving physical activity is a top priority in Rural Healthy People 
2020.13 The prevalence of obesity among Appalachian teens exceeds national rates and is greater than 25%.10-
12 
 
In academic underperforming schools such as those prevalent in Appalachia, the primary educational focus is 
to meet core academic mandates. To meet academic mandates, despite school-based health education and 
physical education programs, most Appalachian schools no longer require health and physical education. Low 
rates of physical activity and high rates of obesity persist in Appalachia. Efforts to improve physical activity in 
school-aged adolescents residing in Appalachia have relied on organized sports and school-based curricula 
delivered by teachers. These approaches have not been effective for Appalachian teens.19 One explanation may 
be unique circumstances present within Appalachia. Opportunities to participate in organized sports are limited 
due to inadequate school resources, lack of available transportation, and limited availability of school teams. As 
a result, most adolescents residing in Appalachia are unable to participate in organized sports. Further, school-
based health interventions are limited in their scope and impact on obesity prevention.22-25 School-based 
interventions typically deliver content as part of a regular course such as health or physical education via 
teachers in classroom settings (this is the proposed comparison intervention). Low efficacy of these programs 
may be due to unique cultural challenges: a preference for informal sharing of information among local residents 
rather than health content delivered by formal teachers. Rarely do adolescent obesity prevention programs use 
peer mentoring. Though school-based interventions increase health knowledge, there is less evidence of the 
effectiveness for health behavior changes leading to obesity prevention.24-26 Longer-term follow-up of health 
behavior and health status outcomes in intervention studies are lacking.24,27 
 
 
Rationale 

Planning to be Active (PBA)2-4 is a theory-based program for promoting regular physical activity in high school 
teens in Appalachia. In pilot studies, PBA was delivered by classroom teachers and showed efficacy in producing 
short-term increases in physical activity. Additionally, the research team has developed and tested a peer 
mentoring program based upon similar theoretical constructs used in PBA to successfully promote daily physical 
activity changes in Appalachian youth.14-15 Building upon these successes and our experience in this highly 
vulnerable population, we propose to expand the PBA intervention to be delivered in an after school program by 
trained high school mentors from Appalachia. We predict that peer mentors will be more effective than classroom 
teachers and will improve their own health behaviors, providing a double-edged intervention.16 Employing high 
school mentors as agents of change further addresses a unique need of Appalachian residents who often lack 
access to or trust in health professionals. 
 
III. 
Procedures: 
A. Research Design 
Our approach is to randomize participants to one of two groups: (1) intervention: Peer Mentoring (MBA), and (2) 
comparison: Teacher-Led (PBA).  We will test the hypothesis that, compared to delivery by teachers in a 
classroom setting (usual care), an innovative delivery format of PBA by local peer mentors (trained 11th and 12th 
grade teens) will promote the adoption of healthier physical activity and regular exercise among 9th and 10th 
grade teens not engaged in organized sports by combining (a) peer mentoring by trained high school students 
to younger adolescents with (b) a tailored self-regulation lifestyle program. We propose a group-randomized 
controlled trial (G-RCT) to evaluate mentored delivery of a school-based intervention to improve physical 
activity behaviors and health outcomes among high school students in Appalachian Ohio. G-RCTs are 
characterized by random assignment of identifiable social groups (rather than individuals) to study conditions, 
with measurements taken on members of those groups to assess the impact of the intervention.44,45 
 
Prior to the intervention (baseline data), we plan to complete a sub-analysis of the relationship between health 
behaviors from lifestyle behavioral scale (sleep patterns reported and sugar sweetened beverage consumption 
rates and measured body mass index and body fat percentage).  
Sample  
We conducted a careful analysis of sample size calculations for primary analysis of the primary outcome: BMI in 
adolescents at 6 months post intervention. As discussed further below, we will use a mixed model ANCOVA in 
our primary analysis. Power calculations for this approach are given by Murray.42 Power in a G-RCT is influenced 
by five factors: number of groups (schools), number of individuals in each group (adolescents in each school), 
similarity of outcomes within clusters (school-level intra-class correlation [ICC] for BMI), similarity of outcomes 
within individuals (correlation of BMI measurements on the same student over time), and percentage of the 
variance that can be explained by the regression model. Using data from the Ohio Family Health Survey, we 
estimate that the school-level ICC for BMI among 9th graders in Appalachian Ohio counties is 0.023. We further 
estimate the over-time correlation of BMI measurements is 0.70 and that adjusting for age and gender will explain 
approximately 30% of the variance in BMI. With these assumptions, 10 schools per condition, and 75 students 
per school, we will have 82% power to detect a modest intervention effect (0.2 standard deviation difference 
between groups). This effect size would correspond to a difference in mean BMI between groups of 1.04 kg/m 2 
if the observed variation in BMI is similar to that of all Appalachian 9th graders from the Ohio Family Health 
Survey (mean BMI = 23.41, SD = 5.2).  
 
With an average of 75 mentee participants per school (50% participation rate of eligible students based on our 
preliminary work), we estimate that a total of 1,500 9th and 10th grade participants will be recruited over 2 years 
(750 per year). Further, 190 older teens will be recruited to serve as peer mentors (19 per school x 10 schools) 
in the MBA condition, for a study total of 1,690 high school-aged children. Recruitment of mentor and mentee 
participants will occur during the start of the school year (September). 
 
At the end of the academic year, we will randomly select at least 1 PBA teacher and 5 teen mentors from each 
school that delivered the curriculum that year to participate in cognitive interviews. These semi-structured 
interviews  will be conducted individually and will be audio-recorded . Audio recordings will be transcribed 
verbatim . Each interview will last approximately 30 minutes and will be held in a private room at each school.  
 
Participant Recruitment and Retention 

Nine school districts from 9 rural Appalachian Ohio counties have agreed to participate; an excess of 10 schools 
needed for Wave1.  Wave 2 schools will be recruited during Year 1 of the study (with amendment to IRB protocol 
once letters of support are received). Peer mentors will be recruited based on interest in working with peers, 
supporting others, and striving to cultivate their own health-supportive behaviors. Eleventh and 12th graders 
attending the target high school at the study's start will be eligible to participate as mentors if they are not 
expected to move from the participating school before the conclusion of the study; can speak English; and are 
recommended by a teacher, school advisor, or counselor. Teens interested in serving as mentors will complete 
an application form; selection will be based on: study needs, motivation to serve, and recommendations. Teen 
mentors with a BMI (for age and gender) above the 85th percentile or below the 5th percentile at the start of the 
study will be excluded because of concerns about those not classified as having a healthy weight serving as role 
models for healthy lifestyle behaviors. These exclusionary criteria will be clear in recruitment materials so those 
excluded understand why.  All teen mentors will attend the same school and reside in the same local community 
as study participants. Each peer mentor will be assigned up to 4 adolescents (2 per session; 2 possible sessions 
each week); we will recruit 19 peer mentors per school. We will attempt to match mentors and mentees by 
gender.  
 
School health teachers at participating schools will provide the comparison delivery method. With approximately 
25 students per classroom, we anticipate 3 classrooms to participate in each comparison school. Based on 
preliminary studies, an 80% retention rate is expected, resulting in 82.5% power to detect a modest intervention 
effect. To aid retention of subjects, monetary incentives at each data collection time-point and booster kits for 
use during the summer months will be provided. In addition, attendance will be taken at all sessions; the PI will 
contact all subjects missing a session within 48 hours.   
IV. 
Measurement/Instrumentation 
Measures. Our measures are comprised of items adapted from published studies and preliminary studies. All 
measures have been used with adolescents residing in rural or Appalachian settings and show acceptable 
psychometrics: internal consistency reliability α of .75-.94; face validity, predictive validity, and content validity, 
and/or construct validity are established (See Table 1). 
Table 1. Variables, Instruments, Reliability, Validity and Data Collection Times for the Teens 
Variable from 
Theoretical 
Framework 
Instrument/ Source of 
Data 
Cronbach's 
Alpha 
Type of 
Validity 
Data Collection Times 
Measure Used in Statistical Analysis 
Pre 
 
3 
mo   
6 
 mo 
Demographics 
(potential 
covariates/ 
moderators) 
Demographic 
questionnaire 
NA 
NA 
X 
 
 
 
Item ratings/answers 
Behavioral 
Outcomes  
Healthy Lifestyle 
Behaviors Scale   
      
Accelerometer Activity 
.84 and ↑ 
Face, 
Content  
Construct   
 
X 
 
 
 
X 
 
 
    
 
    
 
X 
 
  
X 
 
X 
 
 
X 
Total score on the Healthy Lifestyle 
Behaviors Scale  
 
 
7 days of daily activity   
Behavioral 
Outcomes 
Daily Lifestyle Patterns 
 
Content, 
Construct 
X 
   
X    
X 
4-item questionnaire  
Primary Health 
Outcomes 
Body Mass Index, 
BMI%, Body Fat% 
NA 
NA 
 
X 
  
 
 
 
X 
 
X 
BMI, BMI%, Bio Electronic Impedance  
Psychological 
Determinants    
 
Physical Activity 
Outcome Expectations 
 
 
 
Self-Efficacy for 
Physical Activity 
.86-.97 
 
 
 
 
 
    85-.94 
Construct 
Predictive 
 
 
 
 
Predictive 
X 
 
 
 
 
 
X 
  
 
 
 
 
 
 
X 
 
 
 
 
 
X 
X 
 
 
 
 
 
X 
Total Score on each of 8 sub-scales: 
relaxation, fitness, competition, social 
growth, social continuation, health 
Total Score 
 
Self-Regulation 
Self-Regulation for 
Physical Activity 
 
 
 
 
Social Support 
  .89-.95 
 
 
 
 
 
.75-.88 
Content 
and 
Construct 
 
 
Face and 
Construct 
X 
 
 
 
 
 
X 
 
 
 
 
 
 
 
X 
 
 
 
 
 
X 
 X 
 
 
 
 
 
X 
Total score on each 6 subscales: goal 
setting, self-monitoring, overcoming 
barriers, time management, self-reward, 
planned support 
 
 
Total Score 
Environmental 
Determinants 
Perceived Environment 
for Physical Activity 
Test-retest 
(59%-74%) 
Construct 
X 
 
X 
 X 
Total score on each of 1 subscale: 
neighborhood  
 
Demographics 

Demographic Questionnaire. Participants and peer mentors will complete a demographic questionnaire with: 
age, birthdate, grade in school, gender, race and ethnicity, zip code, and household members.  
 
Primary Health Outcomes 
Body Mass Index (BMI): The primary outcome of BMI will be computed by measuring height and weight for 
each participant. BMI measurement procedures are further described in Appendix A. We propose actual BMI as 
the primary outcome because this is a long-term trial. BMI z-scores are widely used and recommended for cross-
sectional comparisons because they take into account differences in age and gender that may confound those 
comparisons. Recently, however, several authors have cautioned against the use of BMI z-scores for research 
using longitudinal designs, citing concerns that their use could result in spurious differences between groups.55,56 
One reason for this problem is that children at the extreme ends of BMI distribution require substantially greater 
changes in weight than their thinner counterparts for the same change in z-score. Because the BMI z-score 
curves were constructed using only data between the 3rd and 97th percentiles, the CDC recommends caution 
when using growth curves outside this range.57 Berkey et al. noted that the difference between z-scores reflect 
larger differences in BMI in older compared to younger children.  
Using CDC guidelines by age and gender, we also will calculate BMI percentiles with healthy weight status is 
defined as between the 25th and 85th percentile; overweight status is defined as between the 85th and 95th 
percentile; and obese is defined as above the 95th percentile.54  
 
Body Fat Percentage: Body fat percentage readings will be obtained by a portable body electronic impedance 
(BEI) device. During each data collection cycle, two measures using BEI will be obtained. The average 
percentage from the readings will be recorded.58,59 Room will be a normal ambient temperature (about 720 F). 
Participants will lie supine on a mat with non-conductive surface. with arms and legs abducted at 30-45 degree 
angles from the trunk.89 Electrode skin sites will be cleansed with alcohol wipes prior to application. Two distal 
current-introducing introducing electrodes will be placed on the dorsal surfaces of the hand and foot proximal to 
the metacarpal phalangeal and metatarsal phalangeal joints, respectively.89 Two voltage-sensing electrodes will 
be applied at the pisiform prominence of the wrist and between the medial lateral malleoli of the ankle.89 BEI 
measurements will be taken in the morning hours and prior to engagement of MVPA for that day. Participants 
will be instructed to be normally hydrated and urinate within 30 minutes of test.89 See Appendix A for 
measurement procedures. BEI data will be adjusted for height to allow for the estimation of total body water 
(TBW), relating height2/impedance to TBW.90 TBW will be converted to total fat-free mass (FFM) from which 
body fatness will be estimated using equation accounting for height, resistance in ohms, body weight in kg and 
age.91 See Appendix A for complete equations.  
 
Behavioral Outcomes 
Daily Physical Activity: Student, peer mentor, and teacher participants will wear accelerometers for 7 straight 
days (1 week) of physical activity data collection measures for each data collection cycle (described above). The 
data will be used to estimate time spent in sedentary, moderate, and vigorous activity. Readings at or above 
3962 counts per minute will be treated as vigorous physical activity.61,62,92  Moderate physical activity cut points 
are between 1535-3961 counts per minute.92 Readings between 100-1534 counts per minute are light activity.92  
Readings less than 100 counts per minute will be treated as sedentary activity.87,88,92 Two or more hours of zero 
counts suggests that device as not worn and will be excluded from sedentary analysis.92 
Healthy Lifestyle Behaviors Scale: Student and peer mentor participants will respond to 8 items on a 5-point 
Likert scale. Face and content validity are established for physical activity and sedentary behaviors (8 items) 
when used with teens.63 Internal consistency reliability has been reported at α =.78 when used with teens.63  
Daily Lifestyle Patterns: A 4-item questionnaire will be given to ask participants to estimate their daily SSB 
consumption, water consumption, and hours of sleep per day during the past 4 weeks. Internal consistency 
reliability has been reported (α > .80) when used with teens residing in rural Appalachia (Smith & Holloman, 
2014). .  
Psychosocial Determinants 
Outcome Expectations for Physical Activity: The outcome expectancy values instrument in this study 
assesses outcome expectations and their associated expectancies for physical exercise by requesting 
information on six dimensions: relaxation, fitness, competition, social growth, social continuation, and health  
Each of the eight dimensions is measured by five items. Previous studies demonstrated internal consistency, 
reliability coefficient ranging α = 0.86-0.97 when used with Appalachian teens.64,65 Construct validity has also 
been demonstrated through three confirmatory factor analyses using data from our previous work.64,65  
Self-Efficacy for Physical Activity: Self-efficacy will be measured using a previously developed instrument 
with 14 items.65 The instrument has demonstrated predictive validity for boys and girls: 0.23 and 0.27.65 Re-test 
reliability of this scale has been reported to be 0.82.64,65 This instrument has been refined by adding three 
additional items and altering the response scale from dichotomous to a five-point Likert-type scale, with internal 
reliability consistency ranging α = .85-.94.64,65  
 
Self-Regulation 
Self-regulation for Physical Activity Questionnaire contains six subscales: Behavioral Goal Setting, Self-
Monitoring, Overcoming Barriers, Time Management, Self-Reward, and Planned Social Support.66 The 

instrument was developed by Co-PI Petosa using a three-stage expert panel review to establish content validity. 
Internal reliability ranged from 0.89 to 0.95.66 Construct validity has been demonstrated through confirmatory 
factor analyses.66  
Social Support: Social support is measured using a self-report questionnaire containing 13 items originally 
developed by Reynolds et al.67 and refined by Trost et al.68 This instrument measures instrumental social support, 
social encouragement, and social expectations that are provided by friends and family members for physical 
exercise. In order to increase the internal reliability of the instrument, the original reporting scale was expanded 
to a five-point Likert-type scale.37 This instrument has previously been demonstrated to have construct 
validity.177,178 Internal reliability ranged α = .75-.88, and re-test reliability ranged R = 0.78-0.93.64,65  
 
Environmental Determinants 
Perceived Environment for Physical Activity: Perceived environment is measured by one Likert scale. The 
8-item subscale focuses on the perceived neighborhood environment. Construct validity and test-retest reliability 
have been established for this scale when used with adolescents residing in rural communities.69  
 
Acceptability, Ease of Delivery, and Usefulness of Curricula Delivery Approach 
Cognitive Interviews: Semi-structured interviews will be conducted on a randomly selected subset of mentor 
and teacher participants. Questions focus on acceptability, ease of delivery, and usefulness of curriculum. 
   
B. Detailed Study Procedures 
Anthropometric and other quantitative data will be in a designated private room at each participating high school 
at all data-collection time-points (T1, T2, and T3). Questions will be read aloud to accommodate any reading 
difficulties and be available to answer questions. Prior to baseline data collection, a series of training sessions 
will be held to train data collection staff responsible for taking measurements, including physical activity using 
Actical accelerometers (Actical, Bio-Lynx Scientific Equipment, Montreal), BMI,  body fat, and administration of 
the data collection instruments. The measurement team training will held at the Project Office located on the 
OSU campus. In order to assure standardization and quality of data collection, this training will include a review 
of the eligibility criteria and consent procedures; overview of the measurement protocol; demonstration of the 
measurement methods; and an opportunity to have each measurement team mock data collect on several 
subjects and gain expert feedback on their ability to follow protocol. This training will occur prior to each data 
collection time period (T1, T2, and T3). The accelerometer data collection training will be done by Dr. Petosa. Dr. 
Petosa is an expert in accelerometer use and accelerometer data processing. Body fat analysis measurement 
training will be conducted by Dr. Smith an advanced practice registered nurse and expert in the collection of 
anthropometric data in community settings. The survey data collection training will be conducted by Petosa and 
Smith. The Project Office will ensure that the measurement teams perform only those functions for which they 
are certified, and that re-certification activities are implemented as planned in a timely manner.   
 
Participants will receive $15 at each data-collection time-point. Anthropometric measures (height, weight, and 
body fat) will be collected from each privately. BMI will be calculated by: weight (lb)/[height (in)]2 x 703 
(Measures). Pilot testing found anthropometric data collection will take 10-15 minutes. BMI percentiles for 
children will be calculated via computer using CDC guidelines. Data from peer mentors will be collected by an 
RA at completion of mentor training (T1), at the end of the academic year (T2), and beginning of the following 
academic year (T3). See Table 5 (next page) for the data collection plan and process.  
 
In adherence to school and district policies, teacher participants will not be provided any financial incentive for 
their participation.  
 
Informed consent will be documented on a participant tracking form. This form allows linkage of the participant 
to their study identification number. For subsequent data collection, the identification number will be used instead 
of any identifiable information. All databases will be encrypted and password-protected. We will use a registration 
and tracking number that is randomly generated to document all measurement activities within individuals. Once 
informed consent is given, information on physical activity levels will be collected using the Actical accelerometer. 
Collection of the physical activity variable requires that the Actical be worn by the study participant for seven 
study days, and requires two school visits by the measurement team for each participant. Participants will be 
informed of the procedure used to wear the Actical monitor as outlined in the Actical user manual. There will be 
a demonstration of the correct right iliac crest placement of the monitor using a belt provided by the study. Each 
subject will receive a belt that allows for the correct placement of the monitor and written directions for the subject 
and parent to assure correct placement of the device in subsequent days. The subject will receive a reminder 
daily from their classroom teacher during the monitoring period to make sure that they are wearing the monitor 
and to answer any questions they may have. After seven days of monitoring, the measurement team will return 
to the school collect the devices, debrief the participant, and provide an incentive. Actical monitor data will be 
downloaded to a laptop PC at the school site by the measurement team. This requires the use of the download 
device and software provided by the vendor. The monitor will then be reset and redistributed for use by another 

subject in that school. This process will be repeated weekly for three weeks at each school, representing three 
waves of data collection in a different one-third of the participating students each week.  
 
One week prior to implementation of the intervention, a measurement team will go to the school for another data 
collection day that involves survey instrument and data collection. Each data measurement team will read explicit 
directions regarding how the survey will be filled out. With each instrument the measurement team will read the 
directions, answer questions, and then allow the students to fill out each instrument. When the entire battery is 
completed, the measurement team will collect the data, count the surveys, and record the count on a data sheet 
that is placed in a box and sealed. This box will not be opened until it reaches the project office to be counted to 
verify there are no missing forms.  
 
The questionnaire will administered in a group setting each day for a week at the beginning of the health class 
period using a paper-and-pencil format. Filling out the survey takes approximately 10 minutes, and each day it 
gets easier and faster to fill out as the students become accustomed to it. On Friday, the students are given two 
questionnaires to be filled out as homework each night before bed and to be signed by a parent as completed 
on that date. This will provide for a seven-day total of activities the student recalls. Again, when the entire group 
of questionnaires is completed, the measurement team will collect the data, count the surveys, and record the 
count on a data sheet that is placed in a box and sealed. This box will not be opened until it reaches the project 
office to be counted to verify there are no missing forms. Questionnaires and data forms will be kept in a locked 
cabinet in the project office. Demographic information or identifiable data will be removed immediately from the 
questionnaire, transported, and stored separately from the questionnaire.  
Table 5: Data Procedures and Process 
Time 
Process:                                                                                (Data Provided By) 
Team Member  
YEAR 1 
 
 
 
Finalization of data collection protocols 
Finalization of participating schools to Waves 1 or 2 
Training of PD and RAs: protocols, use of accelerometers  
Randomization of Wave 1 schools to either intervention or comparison  
Smith & Petosa 
Smith & Petosa  
Smith & Petosa 
(Shoben) 
WAVES 1,2 
 
 
Consent 
Consent/Assent of Peer Mentors and School Participants 
Smith or Petosa 
End of 
Recruitment 
1. 
Assignment of identification numbers to subjects, mentors, and teachers  
2. 
Demographic Questionnaire                                         (Peer Mentor, Subjects 
Szalacha 
Team 
Baseline (T1) 
 
Subject Survey Questionnaire: Self-Report                                                          (Subjects) 
Height, weight, BEI                                                                                               (Subjects) 
Accelerometer – one week of dataa                              (Teachers, Peer Mentors, Subjects) 
Health Behaviors Questionnaire:                                                   (Peer Mentors, Subjects) 
Team  
T1: Follow-Up  
Collection of accelerometer data (7 days) 
Team 
Weekly 
Debriefing sessions: observations and notations: mentors and teachers 
Measures of Fidelity (mentors and teachers) 
Data Interim Reports 
Smith & Petosa 
 
Monthly 
Measures of Fidelity (mentors and Teachers) 
Smith & Petosa  
3 Months Post 
Intervention 
(T2) 
Subject Survey Questionnaire: Self-Report                                                          (Subjects) 
Height, weight, BEI                                                                                                (Subjects) 
Accelerometer – one week of dataa                              (Teachers, Peer Mentors, Subjects) 
Health Behaviors Questionnaire                                                    (Peer Mentors, Subjects) 
Team 
 
T2: Follow-Up 
Collection of accelerometer data (7 days) 
Reinforcement (Booster) Session 
Team 
6 Months Post 
Intervention 
(T3)  
Survey Questionnaire: Self-Report                                                                       (Subjects) 
Accelerometer – one week of dataa                             (Teachers, Peer Mentors, Subjects) 
Health Behaviors Questionnaire                                                   (Peer Mentors), Subjects) 
RA (blinded to group) 
 
T3: Follow-Up 
Collection of accelerometer data 
Team 
 BEI = bio-electronic impedance;  aAccelerometer data collected over 3 weeks (1/3 of subjects each week to wear for 1 week)  
C. Internal Validity 
To avoid study bias, schools will be randomized for study condition (PBA or MBA).  The primary analysis will 
follow intention-to-treat principles.77,78 Randomization carries the expectation that the study conditions will be 
equivalent at pretest with respect to known and unknown prognostic factors. As a result, removing randomized 
groups or members from the analysis runs the risk of tampering with this balance and introducing bias. Further, 
loss of one or more groups could create an unbalanced design at the group level and heighten the risk associated 
with heteroscedasticity in a G-RCT.79 Based on our previous research, we estimate that no more than 20% of 
members measured at pretest will be missing at posttest, although we will make every effort to obtain posttest 
data on all individuals, including those who stop attending the sessions. Multiple imputation is now widely 
regarded as an effective method for replacing missing data,78,80 and we will use this approach, adapted for use 
with a G-RCT.81 We will employ SAS PROC MI and SAS PROC MIANALYZE to implement these multiple 
imputation procedures. Advantages of this analysis are that it carries the nominal Type I error rate across a 
variety of conditions found in G-RCTs;47,50,70,73 it provides results that correspond to the standard ANCOVA, 

except that the standard errors and df for fixed effects are constructed to reflect the extra variation and limited df 
due to the G-RCT design; it often has good power compared to the alternatives;82,83 and the analysis is simple 
to conduct and explain. The only disadvantage is that it provides results in the form of adjusted condition means 
rather than change scores; we do not consider this an important disadvantage.  
 
D. Data Analysis 
We will use Linear Mixed Models (LMMs) and Generalized Linear Mixed Models (GLMMs) to account for various 
levels of correlation among participants.75 Power for this study was based on power for the primary analysis 
comparing BMI outcomes at T2 between the two groups, adjusting for baseline BMI values. Such an approach 
is also known as a “mixed-model ANCOVA.”75 We will implement these models using SAS PROC MIXED and 
GLIMMIX, Version 9.3. 
 
Research questions guiding the analysis of cognitive interview data for this aim are: Were there specific 
components of the intervention that appealed more than others? Were there any specific components which 
were not liked or were objectionable in anyway? Was there an age- or gender-related difference in reactions to 
components of the intervention?  
 
To analyze mediating effects of self-regulation and self-efficacy on health outcomes and M/V physical activity, 
we will use bootstrapping techniques to determine significant relationships between the potential mediating 
variables and outcomes of interest.  
 
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